Who We Are

Babylist is the leading registry, e-commerce, and content platform for growing families. More than 9 million people shop with Babylist every year, making it the go-to destination for seamless purchasing, trusted guidance, and expert product recommendations for new parents and the people who love them. What began as a universal registry has grown into a full ecosystem for new parents, including the Babylist Shop, Babylist Health, and a flagship showroom in Los Angeles. Hundreds of brands in baby and beyond partner with Babylist to engage meaningfully with families during one of life’s most important transitions. With over $1 billion in annual GMV, and more than $500 million in 2024 revenue, Babylist is reshaping the $320 billion baby product industry. We’re helping parents feel confident, connected, and cared for at every step. As we build the generational brand in baby, our mission remains simple: to connect growing families with everything they need to thrive.To learn more, visit www.babylist.com.

Our Ways of Working

Babylist is remote-first with team members across the U.S. and Canada who move fast, think smart, and use AI as part of how they work every day — not as an experiment, as an expectation. We come together twice a year to build the relationships behind the work, and we hire people who are genuinely excited about what's possible and prove it through how they show up.

How We Build

Babylist is in the middle of a fundamental shift in how software gets made, and we are not tiptoeing into it. We are rebuilding our engineering culture around a simple belief: AI changes everything. How teams are structured, how decisions get made, how fast ideas become working software. Our engineers own problems end to end, working directly with product, design, and business partners with short feedback loops and real stakeholder access. We ship, learn, and iterate fast. When something is not working, we throw it out and start over — project failure and personal failure are not the same thing here. AI tools are as natural to our workflow as an IDE or version control. We are not exploring this, we are living it. Our engineers use AI to explore tradeoffs, pressure-test designs, and move from problem to solution in hours instead of days. They generate code with AI so they can stay focused on the decisions that actually require human judgment — not the routine ones. More velocity means more time for craft: better test coverage, stronger architecture, and deeper customer understanding. We hold ourselves to a higher quality bar because of AI, not in spite of it. We are building this playbook in real time, and we are looking for people who want to build it with us. If you have already changed how you work because of AI — or you are ready to — and you care more about shipping something great than following a prescribed process, we should talk.

Our Tech Stack 

• React, Native iOS and Android
• Ruby on Rails
• AWS, Terraform
• MySQL
• Sidekiq
• Redis
• Shopify

What the Role Is

As Babylist’s Senior Engineering Manager, Machine Learning & Discovery+Site Monetization, you’ll lead the team driving the next era of our machine learning and personalization efforts across the product. This is a hands-on leadership role for a manager who remains technically fluent while scaling people, systems, and impact. You’ll manage and grow a high-performing ML-focused pod spanning product recommendations, search, PLPs, and site monetization, with a clear mandate: maximize revenue and user value across surface areas through personalization.

You’ll guide the team on technical direction behind our homepage feed, “add next” experience, and other core personalization surfaces, while building the foundations for long-term ML excellence at Babylist.

Who You Are

• 3+ years of experience as a front-line manager delivering production-grade software or ML systems
• 5+ years of experience as an individual contributor in ML or Data Science, plus proven experience leading teams that build end-to-end machine learning systems, ideally in personalization, recommendations, or consumer-facing ML products
• Deep familiarity with the Python ML ecosystem (e.g., pandas, sklearn, xgboost, PyTorch) and comfortable reviewing designs, unblocking technical challenges, and setting high technical standards
• Experience supervising ML Engineers and/or Data Scientists; bonus points for experience managing Software Engineers
• Strong understanding of the full ML lifecycle, including data pipelines, workflow orchestration (e.g., Airflow), deployment, monitoring, and iteration in production
• Product-minded: passionate about technology, but primarily motivated by solving real user problems and driving business impact
• Experience working with large-scale, real-time or near-real-time data systems
• Comfortable owning and evolving the ML tech stack end-to-end, from data and modeling through production systems
• Background in consumer-facing products; experience in e-commerce, marketplaces, or complex user journeys is strongly preferred
• You're genuinely excited about what AI can do - not just as a concept, but as something you want to get your hands on. At Babylist, every team uses AI daily, and we're looking for people who lean in.

How You Will Make An Impact

• Build and lead the ML & personalization function: Hire, mentor, and support a multidisciplinary team (Machine Learning Engineers, Software Engineers, Product Manager, and occasional Designer), shaping the roadmap, standards, and culture for ML at Babylist
• Own high-impact ML initiatives: Deliver personalization systems that improve discovery, engagement, and monetization across key surfaces, directly impacting business outcomes
• Set technical and algorithmic direction: Define modeling approaches, data strategy, and system architecture in partnership with senior technical leaders, balancing experimentation with scalable execution
• Be hands-on when needed: Review ML/DS designs and code, build proof-of-concept models, and conduct your own data science research to de-risk ideas and accelerate progress
• Develop talent: Coach engineers on technical growth, career progression, and performance, combining strong technical standards with empowerment
• Drive cross-functional alignment: Partner closely with Product, Design, Analytics Engineering, and Data to translate ambiguous business problems into clear technical direction
• Understand the full funnel: Develop a deep understanding of Babylist’s user journeys, user feedback loops, and financial models — and how ML systems influence them end-to-end

About Compensation

We use a market-based approach to compensation. The starting salary range for this role is:

USD - $250,000 to $308,000
CAD - $230,000 to $288,000

Your starting salary will be based on your location, experience, and qualifications, with increases over time tied to performance, role growth, and internal pay equity.

Why You Will Love Working At Babylist

Our Culture

• We work with focus and intention, then step away to recharge
• We believe in exceptional management and invest in tools and opportunities to connect with colleagues
• We build products that positively impact millions of people's lives
• AI tools are as natural to how we work as your IDE or version control — we're not exploring this, we're living it.

Growth & Development

• Competitive pay and meaningful opportunities for career advancement
• We believe technology and data can solve hard problems
• We're committed to career progression and performance-based advancement

Compensation & Benefits

• Competitive salary with equity and bonus opportunities
• Company-paid medical, dental, and vision insurance
• Retirement savings plan with company matching and flexible spending accounts
• Generous paid parental leave and PTO
• Remote work stipend to set up your office
• Perks for physical, mental, and emotional health, parenting, childcare, and financial planning

Important Notices

Recorded Interviews Babylist uses an interview recording tool to record and transcribe interviews for evaluation purposes in accordance with applicable privacy laws. By participating in an interview, you consent to this recording and transcription.

Interview Integrity At Babylist, every team uses AI daily and we love it. During interviews though, we want to see you — your thinking, your problem-solving, your creativity. All interviews and assessments should be completed independently without AI tools or third-party assistance unless we tell you otherwise. We'll always be clear when AI is welcome. Misrepresentation during the process may result in removal from consideration.

Protect Yourself from Scams All official communication comes from the Babylist Talent Team via @babylist.com email addresses. We will never ask for payment, bank information, or personal financial details. If you receive outreach via WhatsApp, Telegram, or a non-Babylist email, it's not us. Verify open roles on our careers page.

Connections at Babylist In line with our conflict of interest policy, please let us know if you have a family member or close personal relationship with a current Babylist employee. This helps us keep our process fair for everyone.

Text Message Updates You may opt in to receive SMS updates about your application. Opting out won't affect your status. Message and data rates may apply. Reply STOP to unsubscribe or HELP for assistance. See our Privacy Policy for details.

About Us

Camus Energy builds software solutions that help new load and generation connect to the grid faster—without sacrificing reliability.

As electricity demand accelerates and clean energy scales, traditional interconnection processes are becoming the bottleneck. Utilities and developers face growing queues, long timelines, and costly upgrades that slow progress across the grid.

Camus enables flexible grid connections that allow new load and generation to connect sooner by planning for and operating within real system constraints. Our platform bridges the gap between grid operators and large load developers, providing a view of time-varying grid capacity for any given new interconnection point. 

We combine high-reliability software experience from companies like Google and Meta with deep power systems expertise across the utility sector. If you’re excited to work at the intersection of infrastructure, software, and climate, we’d love to hear from you.

The Role

We're looking for a Machine Learning Engineer to own and advance the forecasting and predictive modeling capabilities at the heart of the Camus platform. This is an individual contributor role with real technical depth and product influence; you'll be responsible for the full lifecycle of ML model development, from exploratory analysis and model design through to production deployment and monitoring. 

This is not a role where the problem statements are handed to you. You'll work directly with Camus’ teams and external stakeholders to understand their data, define the right questions, and translate messy real-world signals into reliable, production-grade data driven analytics. You'll bring that ground-truth perspective back into product decisions, and work closely within the Engineering team to integrate ML models into our planning and operational workflows. 

The forecasting and predictive modeling problems we're solving often don't have off-the-shelf answers. We work as a tight, technical team that moves with urgency but builds with the discipline that production-grade software demands. If you want to do the most technically interesting ML work in the clean energy space while directly shaping how it becomes a product, this is the role.

What You'll Do

• Design, train, and evaluate predictive ML models with a focus on forecasting and time-series applications
• Conduct exploratory data analysis, feature engineering, and statistical modeling across large structured and unstructured datasets
• Collaborate with Engineering to define ML infrastructure requirements, and deploy and integrate ML models into operational workflows and decision-support tools
• Work cross-functionally with Camus teams to define problem statements and translate business objectives into ML solutions
• Communicate model performance, uncertainty, and limitations clearly to both technical and non-technical audiences
• Champion ML best practices around reproducibility, versioning, and testing

What You'll Bring

• PhD with 3+ years of industry experience, Masters with 5+ years, or Bachelors with 8+ years in Machine Learning, Statistics, Computer Science, Applied Mathematics, or a related quantitative field
• Demonstrated track record of delivering ML models into production environments
• Experience with time-series forecasting methods — including classical approaches (e.g. ARIMA) and modern ML-based methods (e.g. gradient boosting or temporal neural networks) 
• Strong proficiency in Python and core ML/data science libraries (PyTorch, scikit-learn, statsmodels, pandas, etc.)
• Experience with probabilistic forecasting, uncertainty quantification and backtesting
• Ability to translate ambiguous business problems into well-scoped ML projects
• Comfortable operating with autonomy in a small team, balancing speed of delivery with the engineering discipline that production-grade software demands.

Nice to Have

• Experience in the energy sector — e.g. load forecasting, renewable generation prediction, price modeling or grid operations
• Experience with MLOps tooling and infrastructure: cloud platforms, containerization, and model serving patterns 
• Experience with data pipeline tooling, e.g. Airflow, Spark, or Databricks 
• Able to leverage AI code development tools to accelerate development

What We Offer

• Competitive base salary
• Comprehensive benefits, including FSA and 401k for full time employees
• Fully remote workplace with options for in office work in the Bay Area
• Flexible PTO, which we encourage you to use!
• A real impact on climate change - we’re building the world we want to live in and we want you to join us!

The expected base salary for this role is $180,000 - $230,000 annually, depending on experience, skills, and qualifications.

CHAOS Industries is redefining modern defense with a multi-product portfolio that gives the ultimate advantage—domain dominance. The company's products are powered by Coherent Distributed Networks (CDN™), empowering warfighters, commercial air operators, and border protection teams to act faster, adapt rapidly, and stay ahead of evolving threats. 

CHAOS Industries was founded in 2022 and has raised a total of $1 billion in funding from leading investors, including 8VC, Accel, and Valor Equity Partners. The company is headquartered in Los Angeles, with offices in Washington, D.C., San Francisco, San Diego, Seattle, and London. For more information, please visit www.chaosinc.com.

About the Team: Mission Engineering at CHAOS turns simulation output into decisions. We run large-scale modeling and simulation campaigns across all warfighting domains and the full kill chain, against named threats, in operationally relevant scenarios, at the speed engineering, operational, and customer teams actually need. Every CHAOS engineering trade, pursuit, and customer engagement is anchored in rigorous, physics-based, tactically relevant, and statistically valid analysis, and we're scaling the function to meet that bar across a growing product portfolio.

About the Role: On the Mission Engineering team at CHAOS, you will design, develop, execute, analyze, and validate models and simulations to deliver operational analysis and system performance assessment across defense, national security, and commercial programs. You will have freedom to move rapidly and use your expertise to apply rigorous statistical methods to analyze simulation outcomes, quantify system effectiveness, and communicate actionable insights to technical teams, leadership, and customers. Your work will directly inform critical decisions around sensor performance, mission planning, weapon effectiveness, and platform capability evaluation.

This is a foundational hire. You will shape how CHAOS does mission analysis from day one, and you will have the freedom to move fast.

What You'll Do:

• Scope, plan, and execute modeling and simulation efforts and studies aligned to program objectives, managing your competing priorities and timelines effectively.
• Develop, configure, and execute analysis using mission-level models and simulations such as AFSIM, ESAMS, Brawler, Ansys STK, and/or physics-based models to evaluate system and platform performance in operationally relevant scenarios.
• Build and refine models of sensors, communications systems, aircraft (including unmanned), weapons, and spacecraft within mission-level simulation frameworks.
• Architect, implement, and validate models of both blue and red systems, sensors, communications, weapons, aircraft, spacecraft, and ground/maritime platforms, within mission- and engagement-level simulation frameworks.
• Define and apply rigorous statistical methodologies to simulation results to quantify measures of effectiveness and performance to produce relevant and defensible findings for internal and external stakeholders.
• Develop and maintain Python-based analysis tools and pipelines for data processing, statistical analysis, and visualization.
• Implement technical methodology, simulation best practices, and customer engagement.
• Document methodology, assumptions, and findings in technical reports and briefing materials, including data visualizations, and present results to both technical and non-technical audiences.
• Partner with engineering, product, growth, and customer teams to translate operational questions into well-structured analytical approaches and shape new business pursuits. Push back when the framing is wrong.

Minimum Requirements:

• Bachelor's degree or higher in Computer Science, Aerospace/Software/Computer Engineering, Data Science, Statistics, Mathematics, Operations Research, Physics, or a related field.
• 5+ years of experience supporting defense, intelligence, or advanced technology programs through industry or government employment.
• Demonstrated track record leading mission-level operations analysis studies and delivering decision-quality results to senior engineering and customer stakeholders.
• Extensive direct experience with combat simulations such as AFSIM, ESAMS, Brawler, Ansys STK, or equivalent, including ownership of rigorous methodology, not just execution.
• Significant experience developing analytical tools and applying rigorous statistical methods to interpret and present results.
• Strong proficiency in Python and MATLAB, including standard scientific computing libraries (such as Pandas, Polars, NumPy, SciPy, Scikit-Learn, Matplotlib, Seaborn, CuPy, or PyTorch).
• Exceptional data visualization skills and comfort developing CONOPS/OV-1 visuals and briefing packages.
• Proficiency with software development practices including version control and leveraging/validating outputs of AI-assisted coding tools.
• Demonstrated ability to lead analytical efforts and deliver products with minimal oversight, including leading projects and studies.
• Comfortable working on Linux-based systems and writing scalable scripts and software.
• Exceptional written and verbal communication skills, including experience briefing flag/general officer or SES-level audiences.
• Eligibility to obtain a Top Secret / Sensitive Compartmented Information (TS/SCI) clearance 

Preferred Requirements:

• Active TS/SCI clearance.
• Recognized expertise analyzing the full kill chain (find, fix, track, target, engage, assess) across multiple domains, including air-to-air, air-to-surface, surface-to-air, space, and electronic warfare engagements.
• Deep expertise in sensor performance analysis (especially radar) and in developing and validating models of sensors, communications, weapons, aircraft, spacecraft, or ground/maritime systems in mission-level simulation environments such as AFSIM or Ansys STK.
• Experience leading study teams or technical working groups across organizations, including model accreditation, V&V efforts, or threat-informed scenario development.
• Experience designing and analyzing large-scale Monte Carlo and DOE-driven simulation campaigns supporting system effectiveness or kill chain assessment.
• Exposure to Distributed Interactive Simulation (DIS) networking and visualization tools such as MAK VR-Forces, SIMDIS, or similar.
• Experience supporting rapid development programs across DoD services, DARPA, ONR, AFRL, COCOMs, the acquisition community, or commercial advanced technology programs.
• Experience supporting capture and positioning activities (RFI/RFP responses, white papers, technical exchanges) and shaping new business pursuits.

Why CHAOS?

• Health Benefits: Medical, dental, and vision benefits 100% paid for by the company
• Additional benefits: 401k (+ 50% company match up to 6% of pay), FSA, HSA, life insurance, and more
• Our Perks: Free daily lunch, ‘No meeting Fridays’, unlimited PTO, casual dress code
• Compensation Components: Competitive base salaries, generous pre-IPO stock option grants, relocation assistance, and (coming soon!) annual bonuses
• Team Growth: 250 employees and counting across 5 global offices

The stated compensation range reflects only the targeted base compensation range and excludes additional earnings such as bonus, equity, and benefits. If your compensation requirements fall outside of the range, we still encourage you to apply. The salary range for this role is an estimate based on a range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. 

Recruiting Agencies: CHAOS Industries does not accept unsolicited resumes or outreach. Unsolicited submissions will not be reviewed or compensated.

#LI-onsite

CHAOS Industries is redefining modern defense with a multi-product portfolio that gives the ultimate advantage—domain dominance. The company's products are powered by Coherent Distributed Networks (CDN™), empowering warfighters, commercial air operators, and border protection teams to act faster, adapt rapidly, and stay ahead of evolving threats. 

CHAOS Industries was founded in 2022 and has raised a total of $1 billion in funding from leading investors, including 8VC, Accel, and Valor Equity Partners. The company is headquartered in Los Angeles, with offices in Washington, D.C., San Francisco, San Diego, Seattle, and London. For more information, please visit www.chaosinc.com.

About the Team: Mission Engineering at CHAOS turns simulation output into decisions. We run large-scale modeling and simulation campaigns across all warfighting domains and the full kill chain, against named threats, in operationally relevant scenarios, at the speed engineering, operational, and customer teams actually need. Every CHAOS engineering trade, pursuit, and customer engagement is anchored in rigorous, physics-based, tactically relevant, and statistically valid analysis, and we're scaling the function to meet that bar across a growing product portfolio.

About the Role: You will own statistical methodology for the Mission Engineering team at CHAOS. You'll design experimental constructs that extract meaningful signals from broad trade studies and computationally expensive simulation runs, build the analytical pipelines the team relies on, push the methodological state of the art on how we characterize uncertainty, build surrogate models, and communicate quantitative results to decision-makers. You will work shoulder-to-shoulder with engineers and experts in every domain to ensure that simulated, experimental, and tactical results presented by CHAOS are rigorous, reproducible, and actually deliver answers that our teams, customers, and partners need

This is a foundational hire. You will have the freedom to move fast and set the standards for how CHAOS does quantitative analysis from day one.

What You'll Do:

• Design rigorous experimental constructs (DOE, space-filling designs, adaptive sampling, sequential experimentation) for large-scale simulation campaigns, getting maximum signal per simulation hour across operationally relevant trade spaces.
• Apply advanced statistical methods (such as regression modeling, Bayesian inference, surrogate/metamodeling, sensitivity analysis, uncertainty quantification, and beyond) to simulation output to produce decision-quality conclusions.
• Build and own scalable Python-based data pipelines for ingestion, processing, statistical analysis, and visualization of large simulation datasets.
• Develop ML and statistical surrogate models that accelerate analysis, enable real-time trade studies, and feed mission planning applications.
• Set team standards for data management, reproducibility, and statistical rigor (such as code review, methodology validation, and documentation practices).
• Translate operational and engineering questions into well-structured analytical approaches alongside M&S engineers, threat SMEs, and program staff. Push back when the framing is wrong.
• Author technical reports and briefing materials with clear, honest data visualizations; present quantitative results to senior technical and non-technical audiences in language they can act on.
• Mentor peers and cross-functional teams on experimental design, statistical methodology, and reproducible analysis.
• Support programs spanning DoD services, DARPA, intelligence community, and commercial customers.

Required Qualifications:

• Bachelor's degree or higher in Statistics, Data Science, Mathematics, Artificial Intelligence, a related quantitative field, or equivalent demonstrated expertise in modern statistical methodology.
• 7+ years applying advanced statistical and data science methods, ideally supporting defense, intelligence, or advanced technology programs.
• Deep working expertise in experimental design, regression and Bayesian methods, uncertainty quantification, and surrogate modeling, not just textbook familiarity.
• Strong proficiency working in Python, including scientific computing and ML libraries (especially Pandas, Polars, NumPy, SciPy, Scikit-Learn, Statsmodels, PyMC, Matplotlib, Seaborn, CuPy, PyTorch), and exposure to MATLAB or R.
• Demonstrated experience building scalable analytical pipelines for large datasets, including comfort with terabyte-scale data and modern dataframe tooling.
• Exceptional data visualization skills and the ability to develop briefing-quality technical products.
• Strong software development practices: version control, code review, reproducible workflows, and informed use of AI-assisted coding tools.
• Track record of working independently, taking ownership of ambiguous problems, and delivering with minimal oversight.
• Comfortable working with Linux operating systems and writing scalable scripts/software.
• Exceptional written and verbal communication skills, especially in translating quantitative approaches and results for non-technical audiences.
• Eligibility to obtain a Top Secret / Sensitive Compartmented Information (TS/SCI) clearance.

Preferred Qualifications:

• Master's or PhD in Statistics, Data Science, Mathematics, Artificial Intelligence, or a related quantitative field.
• Direct experience applying statistical methods to outputs from military simulations including high fidelity engineering models, war games, and engagement or mission-level combat simulations such as AFSIM, ESAMS, Brawler, or Ansys STK.
• Expertise designing and analyzing large-scale Monte Carlo and DOE-driven simulation campaigns supporting full kill chain or system effectiveness assessment.
• Experience in developing surrogate models, simulations, machine learning, or artificial intelligence models for engineering and operations analysis applications.
• Familiarity with sensor performance analysis (radar, EO/IR, RF, acoustic), weapon effectiveness analysis, or mission-level engagement analysis.
• Experience with HPC environments and distributed computing frameworks (including scalable cloud services and GPU-accelerated computing).
• Leadership experience: mentoring or leading project teams through complex analytical efforts.
• Substantial experience in communicating statistical methods to both technical and non-technical stakeholders and decisionmakers.
• Experience supporting rapid development programs for DoD contractors, combatant commands, research labs, and acquisition communities.
• Active TS/SCI clearance.

Why CHAOS?

• Health Benefits: Medical, dental, and vision benefits 100% paid for by the company
• Additional benefits: 401k (+ 50% company match up to 6% of pay), FSA, HSA, life insurance, and more
• Our Perks: Free daily lunch, ‘No meeting Fridays’, unlimited PTO, casual dress code
• Compensation Components: Competitive base salaries, generous pre-IPO stock option grants, relocation assistance, and (coming soon!) annual bonuses
• Team Growth: 250 employees and counting across 5 global offices

The stated compensation range reflects only the targeted base compensation range and excludes additional earnings such as bonus, equity, and benefits. If your compensation requirements fall outside of the range, we still encourage you to apply. The salary range for this role is an estimate based on a range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. 

Recruiting Agencies: CHAOS Industries does not accept unsolicited resumes or outreach. Unsolicited submissions will not be reviewed or compensated.

#LI-onsite

At Counterpart Health, we are transforming healthcare and improving patient care with our innovative primary care tool, Counterpart Assistant. By supporting Primary Care Physicians (PCPs), we are able to deliver improved outcomes to our patients at a lower cost through early diagnosis and longitudinal care management of chronic conditions.

We’re looking for a Senior Machine Learning Engineer to help us build a revolutionary new health care business. Clover uses Machine Learning/Natural Language Processing to leverage our data to help keep beneficiaries healthy and out of the hospital by getting them targeted care. By predicting avoidable adverse events, our ML/NLP/LLM infrastructure is central to working on our central mission, and has a direct impact on our beneficiaries. You will help build systems and tools that support the data needs of a diverse organization and contribute to the expansion of the Machine Learning/Natural Language Processing/LLM capabilities of our Data Platform.

As a Senior Machine Learning Engineer, you will:

• Design, implement and validate high-reliability, distributed platforms for machine learning,  natural language processing, and LLMs.
• Create, debug, interpret and improve production machine learning and natural language processing models.
• Build the tools and validation processes that help Clover translate insights into action at scale.
• Use existing commercial and open source tools to create a robust production platform.
• Work closely with Clover's Data Science and Engineering teams to ensure that the ML/NLP/LLM Platform is providing real value.
• Document, iterate, and provide tutorials to ensure Data Scientists are able to use your tools easily.

You will love this job if:

• You want to create impact with your work by finding machine learning-driven insights in the data to unlock value and improve health outcomes for real people.
• You are comfortable acting autonomously in ambiguous and changing environments.
• You value collaboration and feedback. You can communicate technical vision in clear terms— to your teammates and across the technology team more broadly. You are willing and able to help your teammates grow by demonstrating best practices, providing (and receiving) respectful and constructive feedback, and disclosing your unique insights with everyone.
• You enjoy working in a fluid environment, defining priorities that adapt to our larger goals. You can bring clarity to ambiguity while remaining open-minded to new information that might change your mind.
• You are not hesitant to jump in to help fix things that are broken and you are encouraged to make sustainable systems. You are happy to fill in the gaps to reach a goal where necessary, even if it does not always fit your job description.
• You have a genuine interest in what good technology can do to help people and have a positive attitude about tackling hard problems in an important industry.

You should get in touch if:

• You have 5+ years of experience in Machine Learning Engineering roles in technology enabled companies. Healthcare experience preferred but not required.
• You have experience with Python, Python data science libraries (numpy, pandas, sklearn, tensorflow, pytorch, etc.), and deploying Python apps into production environments.
• You have experience with Natural Language Processing and/or LLMs.
• You have a solid foundation in feature engineering, feature selection, and machine learning techniques.
• You have experience interpreting, modifying, and debugging the inputs and outputs of production ML/NLP/LLM models.
• You have both built and refactored complex distributed systems, especially ML/NLP/LLM systems.
• You have scaled the impact of other engineers and data scientists through mentorship, development of reusable libraries, and documentation.

Benefits Overview: 

• Financial Well-Being: Our commitment to attracting and retaining top talent begins with a competitive base salary and equity opportunities. Additionally, we offer a performance-based bonus program, 401k matching, and regular compensation reviews to recognize and reward exceptional contributions.
• Physical Well-Being: We prioritize the health and well-being of our employees and their families by providing comprehensive medical, dental, and vision coverage. Your health matters to us, and we invest in ensuring you have access to quality healthcare.
• Mental Well-Being: We understand the importance of mental health in fostering productivity and maintaining work-life balance. To support this, we offer initiatives such as No-Meeting Fridays, monthly company holidays, access to mental health resources, and a generous flexible time-off policy. Additionally, we embrace a remote-first culture that supports collaboration and flexibility, allowing our team members to thrive from any location. 
• Professional Development: Developing internal talent is a priority for Clover. We offer learning programs, mentorship, professional development funding, and regular performance feedback and reviews.

Additional Perks:

• Employee Stock Purchase Plan (ESPP) offering discounted equity opportunities
• Reimbursement for office setup expenses
• Monthly cell phone & internet stipend
• Remote-first culture, enabling collaboration with global teams
• Paid parental leave for all new parents
• And much more!

About Counterpart Health: In 2018, Clover Health set out to do something unprecedented: build a clinically intuitive, AI-enabled solution that fits within physicians' workflows to help support the earlier diagnosis and management of chronic conditions.

Years later, that vision is a reality, with thousands of practitioners using Counterpart Assistant during patient visits to improve disease management, reduce medical expenses, and drive success in value-based care.

With an exceptional team of value-based care and technology experts, Counterpart Health is driving value-based care at the speed of software.

Counterpart Health is a subsidiary of Clover Health. From Clover’s inception, Diversity & Inclusion have always been key to our success. We are an Equal Opportunity Employer and our employees are people with different strengths, experiences, perspectives, opinions, and backgrounds, who share a passion for improving people's lives. Diversity not only includes race and gender identity, but also age, disability status, veteran status, sexual orientation, religion and many other parts of one’s identity. All of our employee’s points of view are key to our success, and inclusion is everyone's responsibility.

#LI-REMOTE

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. We are an E-Verify company.

Local or 100% Remote

About Point

✨ Real Impact, Real People: Our mission at Point is to make homeownership more valuable and accessible. Your work directly helps homeowners access their wealth, achieve financial flexibility, and realize life changing goals.

✨ Funding: With over $175M raised from top investors like Andreessen Horowitz, WestCap, Greylock, and Prudential, we’re scaling fast! You have the opportunity to join us at a pivotal stage.

✨ Game-changing Product: We're building a category defining company in home equity. We’ve earned a 4.7 Trustpilot rating and an A+ from the BBB, a testament to the value we provide to our 20,000+ customers.

✨ Great Place to Work: Our employees love working here! We are a Certified Great Place to Work and a Fortune Best Workplaces in the Bay Area.

✨ Remote First Culture, Genuine Connection: Work from anywhere in the U.S., while staying closely connected through virtual collaboration, team gatherings, and a people-first culture.

About the role

We are seeking a Senior Data Scientist to join our growing data team. You will be responsible for building and deploying machine learning models to support marketing, customer engagement, and core business use cases. You will also monitor model performance, identify and mitigate model deterioration, forecast business outcomes, and maintain high-quality datasets. The role is not limited to working on machine learning and model development and will also include, but not limited to advanced analytics projects such as root-cause analysis, identifying trends in data and building time-series projections.

This is an excellent opportunity for someone advanced in their data science career to enhance their skills in applied machine learning while working closely with other senior data scientists, engineers, and cross-functional stakeholders.

Your responsibilities

• Model Development: Design, train, and deploy machine learning models to solve marketing, forecasting, and operational problems.
• Model Monitoring: Track model performance, detect drift or deterioration, and assist in implementing corrective measures.
• Forecasting & Insights: Develop time-series forecasts and performance dashboards that inform marketing strategies and broader business decisions.
• Data Management: Clean, transform, and maintain structured datasets to ensure reliable inputs for analytics and modeling.
• Collaboration: Partner with marketing, product, and business teams to translate requirements into data-driven solutions.
• Documentation & Reporting: Produce clear documentation of models, data pipelines, and processes; communicate insights to technical and non-technical stakeholders.

About you

• 4–5 years of hands-on experience in data science, applied statistics, or machine learning (internships and projects count).
• Familiarity and experience working with AI toolkits such as Claude or Gemini or similar.
• Strong foundation in Python (Pandas, NumPy, scikit-learn; PyTorch or TensorFlow a plus).
• Familiarity with SQL for data extraction and transformation.
• Experience with basic machine learning workflows: preprocessing, feature engineering, model training, validation, and monitoring.
• Understanding of data visualization tools (e.g., Tableau, Power BI, or Python libraries like Matplotlib/Seaborn).
• Curiosity and eagerness to learn — especially around model monitoring, forecasting, and marketing analytics.
• Excellent problem-solving skills and ability to communicate insights clearly.
• Bachelor’s degree in Computer Science, Statistics, Mathematics, Economics, or a related field. A Master’s degree is a plus.
• Advanced knowledge of AI tools
• Expertise on BI dashboarding tools such as Sigma or Tableau preferred
• Strong Excel skills

Our benefits 

• Generous health benefits: We provide comprehensive medical, dental, and vision plans with options for flexible spending accounts (FSA) and health savings accounts (HSA).
• Unlimited paid time off: Recharge with unlimited paid time off and 10 company holidays. 
• Flexible remote and onsite work: Our teams work from many different locations and time zones. We support fully remote work and also have an amazing in-person environment in our downtown Palo Alto, CA HQ. 
• Fully paid parental leave: Point will supplement state Paid Family Leave (PFL) so employees receive 100% of their regular base pay, plus two additional weeks of fully paid leave after state PFL ends. In states without PFL, Point offers up to 8 weeks of paid parental leave. In addition, employees also receive 4 weeks of fully paid transition time, during which you may work 2–3 days per week while receiving full base pay.
• Equity: We offer meaningful equity because we believe in sharing the value you help create. Your contributions directly impact our growth, and your equity gives you a stake in our future success. 
• Financial wellness:  We provide 401K retirement plans for employees as well as guaranteed life insurance and short- and long-term disability coverage. 
• Extra work/life benefits: We provide monthly stipends for internet, mobile plans, wellness perks, a one-time home office reimbursement, and company provided equipment including a MacBook and monitor.

Compensation at Point will be determined by skills, experience, and geographic location. Point has identified the expected annual base salary for this role at this level based on the market by tiers (Region | Location | Market Salary):

• Tier 1 | San Francisco Bay Area, New York, and Seattle | $179,550 - $198,450
• Tier 2 | Chicago, Austin, Denver, Boston, Washington DC, San Diego, Portland, Sacramento, Philadelphia, Los Angeles & Santa Barbara | $164,350 - $181,65
• Tier 3 | All other US metro areas | $156,750 - $173,250

This does not include any other potential components of the compensation package, including equity, benefits, and perks outlined above. At the launch of each position, we benchmark compensation to the appropriate role and level utilizing competitive compensation data from various data sources as references. At the offer stage, we use the signal we received from our interviews, coupled with your experience, location, and other job-related factors, to determine final compensation.

Location Requirement: This is a remote position. However, candidates must reside in one of Point’s states of operation: AL, AZ, AR, CA, CO, CT, DC, FL, GA, IL, KS, KY, MA, MD, MI, MN, MO, NH, NV, NJ, NY, NC, OH, OR, PA, SC, TN, TX, UT, VA, WA, WI.

Point is proud to be an equal-opportunity employer. We provide employment opportunities regardless of age, race, color, ancestry, national origin, religion, disability, sex, gender identity or expression, sexual orientation, veteran status, or any other protected class. Each individual at Point brings their own perspectives, work experiences, lifestyles, and cultures with them, and we believe that a more diverse team creates more innovative products, provides better services to customers, and helps us all grow and learn. 

SOFTWARE ENGINEER II – DATA ENGINEERING

As a Software Engineer II – Data Engineering based at Rocket Lab’s site in Auckland, New Zealand, you will support the development and deployment of core data services that power engineering, manufacturing, operations, and R&D. You will work on backend services, APIs, data pipelines, and data visualization tools that enable teams across the company to extract value from their data. To be successful in this role, you must be proactive, collaborative, technically strong, and motivated by solving complex engineering challenges. Join a driven team of pioneers relentlessly tackling challenging projects, delivering on the impossible, and shaping the future of space.

WHAT YOU’LL GET TO DO

• Develop APIs, services, and tools that support data indexing, retrieval, visualization, and automation across the company
• Build and maintain data pipelines for engineering, reporting, and operational use
• Develop new software and tooling that support data management and data exploitation
• Maintain and improve existing software products with strong testing and documentation
• Participate in code reviews, architecture design, requirements development, and backlog refinement
• Keep work tickets current and communicate progress clearly to stakeholders
• Demonstrate software to internal teams and coordinate cross‑functionally
• Deploy and support operational software in production environments
• Train and support internal users of data engineering tools and services

YOU’LL BRING THESE QUALIFICATIONS

• Diploma, certificate, or degree in Software Engineering, Computer Science, Electrical Engineering, or equivalent experience
• Minimum five years of experience in a software engineering discipline
• Strong Python development experience
• Experience with Git, CI/CD, and containerization
• Exposure to modern data platforms including data warehouses, datalakes, and lakehouse architectures
• Experience with data analysis tools such as Pandas, NumPy, ETL pipelines, and SQL
• Experience developing REST APIs and backend services
• Familiarity with Jira, Redmine, or similar issue‑tracking tools
• Strong communication skills and the ability to work effectively across teams and time zones
• Ability to assess risk, plan work, prioritize effectively, and take ownership of deliverables

THESE QUALIFICATIONS WOULD BE NICE TO HAVE

• Experience with Kubernetes and deploying services at scale
• Experience with data visualization tools or frameworks
• Knowledge of data science or numerical analysis techniques
• Experience with agile development practices
• Experience in web application development
• Experience in user interface development

NEUTRON
Rocket Lab’s Neutron team is responsible for bringing our new large rocket Neutron and its launch and test sites to life. From designing and testing Neutron’s structures and components, to firing up its new Archimedes engines, to setting up Neutron’s launch pads and test sites across the United States, joining the Neutron team is your opportunity to help launch a new large rocket for the very first time.

SENIOR AERODYNAMICS ENGINEER

Rocket Lab’s integrated analysis team contributes to the operation and development of Neutron by using modelling and simulation to solve challenging problems involving dynamics, fluid flow, and thermodynamics. Whether it is using CFD simulations, writing code to model flow physics, or applying data analysis on flight measurements to develop vehicle performance, analysis is critical to guide changes and develop the capability of Neutron. As a Senior Aerodynamics Engineer in Rocket Lab’s Thermofluids Analysis team you will contribute to the analysis, design validation, and improvements of Rocket Lab’s launch vehicles. 

Day to day, you will be expected to lead concept development, perform trade studies, run simulations, and evaluate performance around Neutron’s aerodynamics, heating, plume interaction and compartment venting. You will be responsible for evaluating aerodynamic control, flow feature characterisation, and representing these by generating aero-table databases. 

Join a driven team that’s tackling challenging projects, delivering on exciting missions, and making an impact on the future of space!

WHAT YOU’LL GET TO DO:

• Perform, validate, and document multi-physics analyses of launch vehicle aerodynamics and provide support for development projects, including: 
  • Identify and leading the implementation of changes to vehicle aerodynamic design and characterisation
  • Performing trade-off studies 
  • System design and optimization 
• Aerodynamic performance and flight dynamics across ascent, re-entry, descent glide and landing 
• Aerodynamic control, operation envelopes and unsteady flow features 
• Represent aerodynamic characteristics through aero tables and models 
• Own aerodynamic loads and environments 
• Own Compartment venting design and environments 

YOU'LL BRING THESE QUALIFICATIONS:

• A degree in Aerospace, Mechanical Engineering or similar field 
• 5+ years industry experience, ideally from the Aerospace, Automotive or Marine industries  with an aerodynamic or fluid dynamics focus
• Excellent understanding of physical principles surrounding aerodynamics, heat transfer, dynamics, internal flow etc.) 
• Demonstrated experience with one or more industry standard CFD packages: Fluent, CFX, STAR-CCM+, etc. 
• Experience in Python and associated scientific packages: numpy, pandas, scipy, matplotlib 
• The ability to work to deadlines within a high-pressure environment  
• Proven communication skills and the ability to build strong working relationships with colleagues and engineering professionals  

THESE QUALIFICATIONS/SKILLS WOULD BE NICE TO HAVE:

• Aerodynamic experience around Controllability, performance and flight dynamics 
• Generating aerodynamic databases for control and guidance from combination of high and low fidelity sources 
• Experience with mechanical systems in a relevant field (e.g. aerospace, automotive, marine)  
• Experience in python for mathematical modelling, analysis tool development and data handling (numpy, scipy, pandas, git, package deployment, continuous integration) 
• Experience with experimental test methods, sensor selection and Thermal protection system design, specification and implementation. 
• Experience with one or more industry standard thermal modelling packages: Thermal Desktop, THERMICA, NX SST 
• Familiarity using Siemens CAD and PDM packages (or comparable industry standard packages) with emphasis on model interrogation and defeaturing for subsequent analyses 
• Familiarity with industry standards (e.g., MIL-STD-1540, GSFC-STD-7000, ECSS-E-HB-32, NASA-STD-5001)  

If you meet some or all of the above requirements apply now, all applications are reviewed and considered.

Position

Are you a passionate Software Engineer looking to bring cutting-edge software solutions to drug discovery? Join our mission to bring novel therapeutics to patients and be at the forefront of driving innovation through technology. As a Software Engineer 2, you will leverage your expertise to develop and implement software solutions that drive our groundbreaking scientific initiatives.

About You

The ideal candidate for this role thrives at the intersection of software and scientific discovery. You are proficient with production-quality Python codebases and can contribute to, maintain, and extend these codebases in a robust and scalable way. At the same time, you understand the fast pace of scientific discovery, and can collaborate with scientists to clarify requirements, focus on result rather than implementation, and keep projects moving forward. You are a strong communicator, especially to non-engineers, as well as a strong team player, always ready to pitch in to help a colleague. You are a continual learner with a growth mindset, eager to learn from your colleagues and expand your talents in a supportive environment and are excited about our mission to bring drugs to patients.

What You’ll Do

• Work with scientists to understand data generated by early drug discovery projects and provide custom analyses and/or production software solutions to answer key questions about the data.
• Build, maintain, and extend tools that deliver machine-learning-based as well as classical algorithmic solutions to scientists.
• Contribute to the continuous improvement of software development processes, tools, and methodologies.
• Conduct thorough code reviews, promoting best practices and identifying opportunities for code optimization and enhancement.
• Collaborate with team members, sharing insights and fostering a culture of knowledge sharing and learning.
• Uphold ethical and data privacy standards in all software engineering activities.

Qualifications

• BS in Computer Science, Mathematics, Physics, Bioinformatics, Biophysics, or a related field, plus 2 years of relevant experience, or equivalent work experience.
• Demonstrated experience (2+ years) with Python; familiarity with scientific programming packages (pandas, numpy) and/or machine learning frameworks (TensorFlow) a plus.
• Undergraduate level biology knowledge required; experience manipulating and analyzing biologically relevant data sets strongly desired (e.g. sequencing data, cellular imaging data, etc).
• Introductory coursework on deep learning or other modern ML approaches, or equivalent work experience, a plus.
• Familiarity with basic software best practices (CI/CDD, unit testing, version control, etc.) highly preferred.
• Strong analytical and problem-solving skills, with the ability to think critically and creatively to overcome technical challenges.
• Effective communication skills to collaborate with diverse teams, both technical and non-technical, especially across disciplines.
• Demonstrated ability to thrive in a fast-paced, dynamic work environment.

At Eikon, employee compensation also includes bonus and equity compensation, in addition to several generous benefit programs, including:​

• 401k plan with company matching​
• Medical (premiums covered by Eikon at 95%), dental and vision insurance (premiums covered by Eikon at 100%)​
• Mental health and wellness benefits​
• Weeklong summer and winter holiday shutdowns​
• Generous paid time off and holiday policies​
• Life/AD&D Insurance (premiums covered by Eikon at 100%) and optional supplemental employee-paid life/AD&D policies ​
• Enhanced parental leave benefit​
• Daily subsidized lunch program when on-site​

The expected salary range for this role is $135,000 to $147,250 depending on skills, competency, and the market demand for your expertise.

Data at Brex

The Data organization develops insights, models, and data infrastructure for teams across Brex, including Sales, Marketing, Product, Engineering, and Operations. Our Data Scientists, Analysts, and Engineers work together to make data—and insights derived from data—a core asset across the company.

What you’ll do

As a senior Data Analyst (DA III), you will own the end-to-end analytics lifecycle for one or more business areas at Brex. You’ll go beyond building dashboards—you’ll frame the right questions, design rigorous analyses, apply statistical methods, and translate your findings into clear recommendations for leadership. You will also serve as a technical leader on the Data Analytics team, mentoring more junior analysts and helping define the standards and best practices that elevate the team’s work.

This role sits at the intersection of analytics, analytics engineering, and business strategy. You’ll work in a modern data stack environment and partner closely with Data Scientists, Data Engineers, and senior leaders across the organization.

Where you’ll work

This role will be based in our San Francisco office. We are a hybrid environment that combines the energy and connections of being in the office with the benefits and flexibility of working from home. We currently require a minimum of three coordinated days in the office per week, Monday, Wednesday and Thursday. As a perk, we also have up to four weeks per year of fully remote work!

Responsibilities

• Own the analytics lifecycle for assigned business areas: from problem framing and data sourcing through analysis, insight generation, and stakeholder presentation.
• Build and maintain dashboards and self-service reporting tools that enable business teams to independently track performance, identify risks, and make data-driven decisions.
• Write production-quality SQL and Python code to extract, transform, and analyze data at scale.
• Collaborate with Data Engineers and Data Scientists to develop and maintain analytical data models, improve data pipelines, and ensure data quality across the organization.
• Partner with leadership across Sales, Operations, Product, Finance, and other departments to identify high-impact analytical opportunities and deliver actionable recommendations.
• Mentor other data analysts and contribute to the development of team standards, documentation, code review practices, and analytical frameworks.
• Proactively identify gaps in data infrastructure, propose improvements, and contribute to the evolution of the team’s tooling and processes.

Requirements

• 5+ years of experience in data analytics, business intelligence, or a related quantitative role.
• 3+ years of experience partnering directly with Sales, Operations, Product, or equivalent business teams as an embedded analytics partner.
• Advanced SQL proficiency, including CTEs, window functions, performance optimization, and working across complex data models.
• Proficiency in Python for data analysis, automation, and modeling (Pandas, NumPy, scikit-learn, or similar).
• Experience with cloud data warehouses, particularly Snowflake (BigQuery and Databricks also valued).
• Hands-on experience with BI and data visualization tools (Looker, Tableau, Hex, or similar).
• Strong stakeholder management skills—proven ability to present complex technical findings to non-technical audiences.
• Experience with generative AI and LLM-based tools (Claude Code, Cursor, GitHub Copilot) to perform and accelerate analyses, automated reporting, and build self-service data tools.

Bonus points

• Demonstrated experience applying statistical methods to business problems (e.g., regression, classification, A/B testing).
• Experience with dbt for data modeling and transformation.
• Experience building and maintaining data pipelines using orchestration tools such as Airflow.
• Experience working with APIs for data ingestion and integration.
• Familiarity with version control systems (Git).
• Experience in fintech, financial services, or payments.
• Track record of leading cross-functional analytics projects from scoping through delivery.

Compensation

The expected salary range for this role is $114,192 - $142,740. However, the starting base pay will depend on a number of factors including the candidate’s location, skills, experience, market demands, and internal pay parity. Depending on the position offered, equity and other forms of compensation may be provided as part of a total compensation package.

Brex LLC is a wholly owned subsidiary of Capital One, N.A.

Data Analytics Team Lead   

Our organization is highly regarded as the industry leader in providing physical therapy/rehabilitation outpatient services.  We have an immediate opening for a Data Analytics Team Lead. We offer a very comprehensive benefit package including Medical, Dental and Vision Insurance, Life Insurance, 401k retirement plan with a company match, generous paid time off (PTO), and much more.  

Team Size: 4–6 (Analysts, Data Engineers, BI Developers)  

Location(s): 100% remote. Preferred locations are Nashville - Chattanooga, TN or greater DFW area, TX.  

We are seeking a strategic and investigative Data Analytics Team Lead to accelerate our journey from reporting to predictive and prescriptive intelligence - unlocking insights that improve patient outcomes, optimize clinic performance, and fuel enterprise growth. Someone who goes beyond the problem and is passionate about identifying and resolving the root case.

You will own Upstream’s enterprise data, analytics, and AI strategy, leading a high-impact team to deliver actionable intelligence across clinical, operational, financial, and patient experience domains. This is a player-coach role: you’ll architect solutions, mentor talent, and translate complex data into executive ready narratives - all while driving measurable ROI.  

The successful candidate will have a thorough working knowledge and understanding of every aspect of Upstream's business operations. They will create dashboards and reports that transform raw data generated by various sources into actionable information that decision makers need to successfully perform their daily duties.  Refined social and communication skills are required that enable effective collaboration with business executives, managers, stakeholders, and subject matter experts, regardless of their level of expertise or technical skills. They must be able to play multiple roles, including architect, report developer, metadata modeler, SQL programmer, and end-user trainer and mentor.  

Success looks like:  

• Reducing claim denial rates by 15% via predictive denial modeling  

• Increasing patient retention by 8% through propensity-to-churn analytics - Delivering 90%+ dashboard adoption among regional directors within 6 months  

DUTIES AND RESPONSIBILITIES:  

The Data Analytics Team Lead will be responsible for:  

Strategy & Vision  

• Define and execute a 3-year analytics roadmap aligned to revenue, cost, and quality KPIs  

• Champion AI/ML adoption (e.g., demand forecasting, therapist scheduling, outcome prediction)  

• Establish data governance, lineage, and quality frameworks (in partnership with IT)  

Team Leadership & Culture  

• Lead, mentor, and grow a team of 4–6 analysts and engineers  

• Build a self-service analytics culture via training, templates, and “Analytics Office Hours”  

• Foster cross-functional partnerships (Clinical, Revenue Cycle, Operations, Finance)  

Delivery & Execution  

• Design and deploy enterprise dashboards, predictive models, and automated insights using Microsoft Power BI + Fabric  

• Integrate data from EHR (RainTree), RCM, etc.  

• Own the full BI lifecycle: requirements → pipeline → visualization → storytelling → adoption  

• Implement real-time analytics (e.g., clinic capacity, no-show prediction) using Azure Data Lake + Synapse  

Innovation & Experimentation  

• Pilot GenAI use cases (e.g., automated narrative reporting, patient summary generation)  

• Explore data mesh architecture for clinic-level autonomy with enterprise governance Performing other duties as assigned.  

REQUIRED EXPERIENCE & SKILLS  

• 8+ years in data analytics, BI, or AI strategy (healthcare or multi-site operations preferred)  

• Expert in Power BI (DAX, Power Query, Row-Level Security, AI visuals, performance tuning)  

• Advanced SQL; proficient in Python (ex: pandas, scikit-learn) or R  

• Hands-on with Microsoft Fabric (Dataflows, Lakehouse, Real-Time Intelligence)  

• Experience with data integration (APIs, ETL/ELT, dbt), data lakes, and real-time pipelines  

• Proven ability to influence executives and train non-technical users  

Nice-to-Have  

• Master’s in Data Science, MIS, or HCI  

• Exposure to healthcare data standards (HL7, FHIR)  

• Experience with RainTree EHR  

• MLOps familiarity (Azure ML, GitOps, etc.)  

• Change management or Agile coaching  

EDUCATION  

Bachelors in computer science, MIS, Statistics, or related field. Equivalent experience accepted.  

TRAVEL REQUIREMENTS:  

Occasional travel to other offices, third-party vendors, or educational conferences may be required.  

Preferred System Experience: Raintree, Waystar, Excel, MS Dynamics, Azure Data Factory, APIs, Azure Data Lake Gen2, Fabric Lakehouse, Databricks, Synapse Analytics, dbt, Power BI, Fabric AI,  

Azure ML, Copilot 

JOIN US!  

If you’re a strategic builder who geeks out on turning rehab data into revenue and lives changed, let’s talk.  

Follow @Lifeatupstream on Instagram, and check out our LinkedIn company page to learn more about what it’s like to be part of the #upstreamfamily.

CLICK HERE TO LEARN EVEN MORE ABOUT UPSTREAM

About Us

Nu is one of the largest digital financial platforms in the world, with more than 122 million customers across Brazil, Mexico, and Colombia. Guided by our mission to fight complexity and empower people, we are redefining financial services in Latin America and this is still just the beginning of the purple future we're building.

Listed on the New York Stock Exchange (NYSE: NU), we combine proprietary technology, data intelligence, and an efficient operating model to deliver financial products that are simple, accessible, and human.

Our impact has been recognized by global rankings such as Time 100 Companies, Fast Company’s Most Innovative Companies, and Forbes World’s Best Bank. Visit our institutional page https://international.nubank.com.br/careers/  

About the team

Portfolio Solutions is the team within Nu Asset Management responsible for the firm's ETFs and systematic fundos, and the allocation models behind our products and the broader Nubank ecosystem. We believe most of an investor's long-term return comes down to three things: cost, allocation, and discipline. Our job is to deliver all three at scale — which means we treat infrastructure, automation, and AI tooling as core to investment research, not as side projects.

We're looking for a quantitative analyst to strengthen our research, index construction, allocation modeling, and the platform that ties it all together.

What you'll do

• Research, design, and implement indices and systematic strategies across fixed income, equities, and derivatives — including structures such as covered calls, duration barbells, and factor portfolios
• Build and maintain multi-asset allocation models, with attention to efficient replication, tracking error control, and transaction costs
• Run rigorous backtests: no look-ahead, no survivorship, realistic costs. We don't publish a curve without out-of-sample replication
• Help build the quantitative infrastructure of the team: reusable data pipelines, backtest frameworks, monitoring dashboards, and reporting workflows on Databricks. The goal is that every model you build outlives the project it was born in
• Develop AI-powered tools for investment research: LLM-driven analysts on top of our internal datasets, agents for routine analytical tasks, copilots that compress the distance between question and answer for the whole team
• Support the launch of new ETFs — from index methodology to interactions with index prodviders, market makers, administrators, and custodians
• Read papers, replicate results, and tell apart what works from what looks like it works

What we're looking for

• Bachelor's degree in a quantitative field: engineering, math, physics, statistics, economics, computer science, quantitative finance, or equivalent
• 3+ years in quantitative research, systematic asset management, risk, or adjacent roles 
• Strong Python (pandas, numpy, scipy). Comfort writing code that other people will read, run, and extend
• Curiosity about — and ideally experience with — building production-grade analytical infrastructure: pipelines, jobs, dashboards, internal tools
• Genuine interest in applying LLMs and AI agents to quantitative work, not just as users but as builders
• Familiarity with the Brazilian market: NTN-B, IMA, Ibovespa, B3 derivatives, local ETF dynamics
• A collaborative, constructive way of working: you ask for help when stuck, you offer help when others are, you document, you review code, you disagree clearly without making it personal, and you give credit generously
• Ability to communicate quantitative results clearly — to portfolio managers, commercial teams, and ultimately to the end investor

Nice to have

• CFA, CAIA, FRM, or a graduate degree in a quantitative field
• Experience with ETFs, index replication, or benchmark construction
• Hands-on Databricks (Workflows, Delta, Unity Catalog, Databricks Apps), version control discipline, MLOps fundamentals
• Experience designing or shipping internal tools, copilots, or RAG systems

Benefits

• Chance of earning equity at Nubank
• Food/ Meal Card (Vale-Refeição and/or Vale Alimentação)
• Public Transportation Commuting Benefit (Vale-Transporte)
• NuCare – Psychological, Financial and Legal Assistance Program
• Life Insurance
• Medical Plan
• Dental Plan
• NuLanguage – Language Course Program
• Nucleo - Our learning platform of courses
• Extended Parental Leave
• Daycare Allowance
• Parental Consultancy
• Work-from-home Allowance
• Gym Partnerships
• 30 days of paid vacation
• Relocation Assistance Package, if applicable

Hybrid 2-3 times/week: Our hybrid work model brings us to the office at least twice a week, on strategic days designed to maximize team connection and collaboration. For more details, visit https://building.nubank.com/nu-hybrid-work-model/

About the team  

Our Warfighter Systems division is pioneering a future where mixed reality (MR), creates immersive, intuitive, and life-saving technologies. By seamlessly integrating real-time data with cutting-edge visualization tools, we are empowering warfighters with unparalleled situational awareness, enhanced training experiences, and operational superiority. This team is consistently pushing the boundaries of what's possible and shaping a future where technology and defense unite to protect our nation.  If this starts to excite you, we invite you to read further!

About the job  

We are seeking a highly skilled and experienced Systems Engineer to design, develop, and validate sophisticated models for our complex electro-mechanical systems, with a particular focus on sensors, cameras, and displays. The ideal candidate will leverage first-principles physics, empirical data, and advanced mathematical techniques to create high-fidelity, predictive models used across the entire product lifecycle – from concept exploration and design optimization to control system development and virtual testing. This role is critical in accelerating our product development cycles, improving system performance, and ensuring robust and reliable operation. 

What you'll do

• Model Development: 
  • Develop mathematical and behavioral models for various sensor types (e.g., IMU, Lidar, Radar, Ultrasonic, environmental sensors), capturing characteristics such as noise, latency, calibration, field of view, and transfer functions.
  • Create detailed camera models, encompassing optics, image signal processing pipelines, distortion, resolution, dynamic range, and temporal characteristics.
  • Integrate individual component models into comprehensive system-level simulations, accurately representing their interactions and overall system dynamics.
  • Develop and maintain Software-in-the-Loop (SIL) and Hardware-in-the-Loop (HIL) simulation environments. 

• Model Validation & Calibration:
  • Design and execute experiments to gather real-world data for model parameter identification, calibration, and validation.
  • Perform rigorous statistical analysis to quantify model accuracy, uncertainty, and fidelity against empirical measurements.
  • Identify discrepancies between model predictions and real-world behavior and iteratively refine models for improved accuracy. 

• Performance Analysis & Optimization:
  • Conduct trade-off studies using models to optimize system architecture, component selection, and control strategies based on performance, cost, power consumption, and reliability metrics.
  • Predict system performance under various operating conditions and environmental factors.
  • Perform sensitivity analyses to understand the impact of individual parameters on overall system behavior.
  • Support root cause analysis of system issues using simulation. 

• Collaboration & Communication:
  • Collaborate closely with cross-functional teams including Electrical, Mechanical, Controls, Software, and Test Engineers to define model requirements and integrate models into the broader development process.
  • Clearly communicate complex modeling concepts, results, and limitations to both technical and non-technical stakeholders through presentations and detailed documentation. 

• Tooling & Methodologies:
  • Select, evaluate, and implement appropriate modeling and simulation tools and techniques.
  • Develop custom scripts and tools to enhance modeling capabilities and automate workflows.
  • Stay abreast of industry best practices, new modeling methodologies, and emerging simulation technologies. 

Required Qualifications

• 6+ years of experience in system modeling, simulation, and analysis, particularly with electro-mechanical systems.
• Demonstrated experience in developing models for at least two of the following:

• • Sensors: IMUs (accelerometers, gyros), Lidars, Radars, Ultrasonic sensors, microphones, etc. (noise characteristics, drift, latency, sensor fusion).
  • Cameras: Optical models, image processing pipeline models, distortion, noise, dynamic range, rolling shutter effects.
  • Display: See/pass-through near eye display architectures, optical modeling, validations, & human perception 

• Strong theoretical foundation in classical and modern control systems, dynamics, signal processing, and numerical methods.
• Proficiency in primary modeling and simulation tools such as MATLAB/Simulink (Simscape a plus), Python (SciPy, NumPy, Pandas), or C/C++.
• Experience with model validation against experimental data and statistical analysis techniques.
• Excellent problem-solving skills, with an ability to apply first principles to complex engineering challenges.
• Strong communication and interpersonal skills, with the ability to work effectively in a team environment. Strong technical communication skills to collaborate effectively with cross-functional teams. 
• U.S. Person Status: Due to government contract requirements, candidates must be a U.S. Person (U.S. Citizen or Green Card holder).
• Bachelor's or Master's degree in Electrical Engineering, Mechanical Engineering, Robotics, Aerospace Engineering, Physics, or a related quantitative field. 

Preferred Qualifications

• Ph.D. in a relevant engineering or scientific discipline.
• Familiarity with Hardware-in-the-Loop (HIL) and Software-in-the-Loop (SIL) testing methodologies and platforms (e.g., dSPACE, National Instruments).
• Knowledge of advanced control techniques (e.g., adaptive control, optimal control, robust control) and estimation algorithms (e.g., Kalman filters, particle filters).
• Experience with machine learning techniques for model identification, surrogate modeling, or data-driven predictions.
• Exposure to systems engineering processes, requirements management, and configuration control.

About the team  

Our Warfighter Systems division is pioneering a future where mixed reality (MR), creates immersive, intuitive, and life-saving technologies. By seamlessly integrating real-time data with cutting-edge visualization tools, we are empowering warfighters with unparalleled situational awareness, enhanced training experiences, and operational superiority, primarily through our EagleEye augmented reality system. This team is consistently pushing the boundaries of what's possible and shaping a future where technology and defense unite to protect our nation.  If this starts to excite you, we invite you to read further!

About the job  

We are seeking a highly skilled and experienced Systems Engineer to design, develop, and validate sophisticated models for our complex electro-mechanical systems, with a particular focus on sensors, cameras, and displays. The ideal candidate will leverage first-principles physics, empirical data, and advanced mathematical techniques to create high-fidelity, predictive models used across the entire product lifecycle – from concept exploration and design optimization to control system development and virtual testing. This role is critical in accelerating our product development cycles, improving system performance, and ensuring robust and reliable operation. 

What you'll do

• Model Development: 
  • Develop mathematical and behavioral models for various sensor types (e.g., IMU, Lidar, Radar, Ultrasonic, environmental sensors), capturing characteristics such as noise, latency, calibration, field of view, and transfer functions.
  • Create detailed camera models, encompassing optics, image signal processing pipelines, distortion, resolution, dynamic range, and temporal characteristics.
  • Integrate individual component models into comprehensive system-level simulations, accurately representing their interactions and overall system dynamics.
  • Develop and maintain Software-in-the-Loop (SIL) and Hardware-in-the-Loop (HIL) simulation environments. 

• Model Validation & Calibration:
  • Design and execute experiments to gather real-world data for model parameter identification, calibration, and validation.
  • Perform rigorous statistical analysis to quantify model accuracy, uncertainty, and fidelity against empirical measurements.
  • Identify discrepancies between model predictions and real-world behavior and iteratively refine models for improved accuracy. 

• Performance Analysis & Optimization:
  • Conduct trade-off studies using models to optimize system architecture, component selection, and control strategies based on performance, cost, power consumption, and reliability metrics.
  • Predict system performance under various operating conditions and environmental factors.
  • Perform sensitivity analyses to understand the impact of individual parameters on overall system behavior.
  • Support root cause analysis of system issues using simulation. 

• Collaboration & Communication:
  • Collaborate closely with cross-functional teams including Electrical, Mechanical, Controls, Software, and Test Engineers to define model requirements and integrate models into the broader development process.
  • Clearly communicate complex modeling concepts, results, and limitations to both technical and non-technical stakeholders through presentations and detailed documentation. 

• Tooling & Methodologies:
  • Select, evaluate, and implement appropriate modeling and simulation tools and techniques.
  • Develop custom scripts and tools to enhance modeling capabilities and automate workflows.
  • Stay abreast of industry best practices, new modeling methodologies, and emerging simulation technologies. 

Required Qualifications

• 9+ years of experience in system modeling, simulation, and analysis, particularly with electro-mechanical systems.
• Demonstrated experience in developing models for at least two of the following:

• • Sensors: IMUs (accelerometers, gyros), Lidars, Radars, Ultrasonic sensors, microphones, etc. (noise characteristics, drift, latency, sensor fusion).
  • Cameras: Optical models, image processing pipeline models, distortion, noise, dynamic range, rolling shutter effects.
  • Display: See/pass-through near eye display architectures, optical modeling, validations, & human perception 

• Strong theoretical foundation in classical and modern control systems, dynamics, signal processing, and numerical methods.
• Proficiency in primary modeling and simulation tools such as MATLAB/Simulink (Simscape a plus), Python (SciPy, NumPy, Pandas), or C/C++.
• Experience with model validation against experimental data and statistical analysis techniques.
• Excellent problem-solving skills, with an ability to apply first principles to complex engineering challenges.
• Strong communication and interpersonal skills, with the ability to work effectively in a team environment. Strong technical communication skills to collaborate effectively with cross-functional teams. 
• U.S. Person Status: Due to government contract requirements, candidates must be a U.S. Person (U.S. Citizen or Green Card holder).
• Bachelor's or Master's degree in Electrical Engineering, Mechanical Engineering, Robotics, Aerospace Engineering, Physics, or a related quantitative field. 

Preferred Qualifications

• Ph.D. in a relevant engineering or scientific discipline.
• Familiarity with Hardware-in-the-Loop (HIL) and Software-in-the-Loop (SIL) testing methodologies and platforms (e.g., dSPACE, National Instruments).
• Knowledge of advanced control techniques (e.g., adaptive control, optimal control, robust control) and estimation algorithms (e.g., Kalman filters, particle filters).
• Experience with machine learning techniques for model identification, surrogate modeling, or data-driven predictions.
• Exposure to systems engineering processes, requirements management, and configuration control.

Cambridge Mobile Telematics (CMT) is the world’s largest telematics service provider. Its mission is to make the world’s roads and drivers safer. The company’s AI-driven platform, DriveWell® Fusion, gathers sensor data from millions of IoT devices — including smartphones, proprietary Tags, connected vehicles, dashcams, and third-party devices — and fuses them with contextual data to create a unified view of vehicle and driver behavior. Auto insurers, automakers, commercial mobility companies, and the public sector use insights from CMT’s platform to power risk assessment, safety, claims, and driver improvement programs. Headquartered in Cambridge, MA, with offices in Budapest, Chennai, Seattle, Tokyo, and Zagreb, CMT measures and protects tens of millions of drivers across the world every day.

CMT is looking for a creative, collaborative, and highly motivated Machine Learning Intern to help develop applied machine learning capabilities using real-world mobility and behavioral data. This internship is ideal for someone who enjoys solving ambiguous problems, building practical ML workflows, working with messy real-world datasets, and translating technical findings into clear recommendations.

The intern will work on a 12-week applied ML project involving data exploration, labeling strategy, feature engineering, model development, evaluation, and stakeholder-facing analysis. The ideal candidate has strong Python skills, a solid foundation in machine learning, strong initiative, and the ability to make progress in open-ended problem spaces.

Responsibilities:

• Explore real-world mobility datasets and identify useful behavioral, temporal, contextual, and aggregate patterns
• Help define practical data labeling guidelines, quality criteria, and edge-case handling rules
• Build and validate datasets for supervised machine learning experiments
• Develop baseline and improved ML models using Python and standard data science workflows
• Perform feature engineering, model evaluation, error analysis, and iteration
• Assess model performance across relevant data segments and edge cases
• Summarize findings, limitations, risks, and opportunities for technical and product stakeholders
• Produce clear documentation, analysis reports, and final recommendations

Qualifications:

• Currently pursuing a Bachelor’s, Master’s, or PhD degree in Computer Science, Data Science, Machine Learning, Statistics, Engineering, Applied Math, or a related quantitative field
• Strong Python programming skills and comfort working with real-world datasets
• Solid understanding of supervised machine learning, feature engineering, classification metrics, train/test splits, and error analysis
• Experience with common Python data science libraries such as pandas, NumPy, scikit-learn, PyTorch, TensorFlow, or similar tools
• Strong analytical and problem-solving skills, with a bias toward initiative and ownership
• Clear written and verbal communication skills, including the ability to explain technical findings to non-technical stakeholders

Compensation and Benefits:

• Fair and competitive hourly rate based on education level
• Flexible scheduling options depending on role and responsibilities

Additional Perks:

• Feel great working to improve road safety around the world!
• Join one of our many employee resource groups including Black, AAPI, LGBTQIA+, Women, Book Club and Health & Wellness
• CMT will do all that is possible to support our employees and create a positive and inclusive work environment for all!

Commitment to Diversity and Inclusion:

At CMT, we believe the best ideas come from a mix of backgrounds and perspectives. 

We are an equal-opportunity employer committed to creating a workplace and culture where everyone feels valued, respected, and empowered to bring their unique talents and perspectives. Diversity is essential to our success, and we actively seek candidates from all backgrounds to join our growing team. 

We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status or disability state. CMT is headquartered in Cambridge, MA. To learn more, visit www.cmtelematics.com and follow us on X @cmtelematics.

About the Role

We’re looking for a Forward Deployed Marketing Data Scientist to partner closely with our AI Decisioning customers and internal engineering teams to ensure that AI-driven marketing campaigns deliver measurable, compounding impact. This role is uniquely cross-functional: you’ll spend time diagnosing model behavior, tuning ML levers, analyzing incrementality, exploring customer data, and explaining insights to marketers and executives.

Marketing teams come to Hightouch to transform how they operate. Instead of planning campaigns weeks ahead on a calendar, AI Decisioning continuously learns customer preferences and executes 1:1 messaging that adapts in real time. Your mission is to make sure that these AI agents perform at their best—and to help customers understand why they are performing the way they are.

You’ll work alongside ML engineers, product managers, Solutions Consultants, and some of the world’s most recognizable brands to improve campaign performance, debug experiments, and identify opportunities for additional lift. Roughly 30% of your time will be customer-facing and 70% deep analytical and modeling work.

No two days are the same, but you can expect to:

Own diagnostics, insights, and tuning for AI Decisioning campaigns

• Explain why AI Decisioning is driving lift using counterfactuals, incrementality breakdowns, and cohort analysis.
• Debug performance issues, iterate on reward functions, and ensure the agent’s recommendations align with customer goals.
• Investigate experiment setups (send volumes, reachability, channel constraints) and surface actionable recommendations.

Work deeply with data in notebooks and customer warehouses

• Pull down historical data to run exploratory analyses using Polars / Pandas in Jupyter notebooks
• Modify and improve customer feature matrices to unlock deep personalization.
• Conduct deeper warehouse-level SQL analyses when insights aren’t available in the UI.

Build lightweight tooling that enables scale

• Create templates, notebooks, scripts, and repeatable workflows that improve how we analyze performance across customers.
• Identify systemic gaps and influence the direction of ML reporting and introspection.

Communicate ML concepts clearly to non-technical stakeholders

• Present model insights and recommendations to marketers, analysts, and executives.
• Explain how the decision engine handles cold start, message transfer learning, exploration vs. exploitation, and more.
• Partner closely with Solutions Consultants to identify and drive new opportunities for uplift.

What We’re Looking For

• Strong ability to perform deep exploratory data analysis in Python (Polars / Pandas, Jupyter notebooks).
• Ability to write and interpret SQL for customer warehouse analysis.
• High-level understanding of ML modeling concepts (features, hyperparameters, reward functions, training windows).
• Excellent communication skills; able to explain technical reasoning simply and confidently to marketers.
• A customer-first attitude with high ownership and urgency when resolving issues.

Bonus Points

• Experience setting up and analyzing marketing experiments such as A/B, multivariate tests.
• Prior experience in an applied ML, data science, analytics engineering, or forward-deployed role.
• Experience building lightweight internal tools or scripting solutions.

We are looking for talented, intellectually curious, and motivated individuals who are interested in tackling the problems above. We focus on impact and potential for growth more than years of experience. The salary range for this position is $140,000 - $220,000 USD per year, which is location independent in accordance with our remote-first policy.

Interview Process

We have limited inbound applications to one application per candidate. You will be auto-rejected if you apply to multiple roles. Please only apply to the position you are most qualified for.

Our interview process evaluates the most important dimensions of the role: analytical depth, customer communication, first-principles problem solving, and alignment with Hightouch’s values.

Intro Call [15–30m]

Introductory call with either a member of our recruiting team or the hiring manager to get to know each other and see if the role could be a good mutual fit.

Take Home Data Analysis Exercise + Review Session [45m]

You’ll complete a short take-home assignment focused on exploratory data analysis of an example dataset. In the live review, you’ll walk through your approach, explain your reasoning, and present your findings as if you were guiding a customer through your analysis. We’re looking for clarity of communication, rigor of thinking, and the ability to turn data into actionable insights.

Experiment Design & Analysis Session [90m]

A hands-on session where we collaboratively design and evaluate an experiment end-to-end. This includes:

• Defining the experimental goal and how success should be measured
• Identifying the most relevant context and features to use in modeling
• Interpreting example results and diagnosing performance patterns
• Recommending next steps and communicating tradeoffs

Hiring Manager Interview [30m]

Chat with hiring manager about past experiences and future operating preferences to assess fit on company values and operating principles.

IMC Americas, Inc. has multiple openings for Trader (REQ#4461664101) in Chicago, IL and Brooklyn, NY.

Your Core Responsibilities

• Develop, maintain, and enhance mathematical models, strategies, and trading system components for quantitative proprietary trading in collaboration with developers (10%).
• Independently design and implement changes to trading models, strategies, and systems from end-to-end, including conceiving of proposed changes, creating a testing plan, and performing tests, and implementing changes (15%).
• Analyze, interpret, and model financial, economic, and industry data to optimize trading and maximize revenue generation using existing internal tools (15%).
• Create custom analysis tools for use at the firm to perform research beyond available analysis tools (5%).
• Prepare and present high-quality financial analysis and reports following IMC global guidelines (15%).
• Perform daily operational tasks on the trading desk, ensuring systems are running correctly and escalating as needed to designated contacts (15%).
• Supervise and mentor graduate traders and interns (10%).
• Train graduate traders through the firm's traineeship (5%).
• Contributing to planning and prioritization decisions of future projects on their trading desk (5%).
• Monitoring and managing a trading firm's risk exposure (5%).

Partial telecommuting permitted. Multiple positions available. Requisition Number: 4461664101

Your Skills and Experience

This position requires a Bachelor's degree, or foreign equivalent, in Mathematics, Econometrics, Computer Science, Financial Engineering, Physics, or similarly quantitative and mathematical field and one (1) year of experience in the job offered or a related position.

Must have experience in the following skills:

• Developing, maintaining, and enhancing mathematical models, strategies, and trading system components for quantitative proprietary trading in collaboration with developers;
• Operating trading strategies through a range of market circumstances, having a thorough understanding of risk at any point in time;
• Rolling out and verifying new features on trading strategies;
• Managing a trading firm's risk exposure;
• Using Python and python coding libraries, including Pandas, NumPy, and SciPy to assist in quantitative analysis; and
• Experience trading products and navigating compliance regulations in trading markets. 

Requires FINRA Certifications: (1) SIE and (2) Series 57.

#LI-DNI

About Wholesail ⛵️

Wholesale trade, a $55T global market, still operates like the world before the internet and modern payment networks: collection processes are manual and run on paper, short-term credit risk is assumed by the vendor, and businesses rely heavily on legacy ERP systems that don't talk to each other. The result is large, measurable drag — we estimate that vendors spend over $500B a year globally across industries on an inefficient mix of processing, bad debt, lending, credit insurance, software, and labor costs to get paid.

Wholesail is building a financial network from the ground up that connects the systems of vendors and buyers involved in wholesale trade to enable streamlined payment and the transfer of risk to third parties. This will allow vendors to offload risk and eliminate tens of billions of waste — while giving creditworthy buyers better terms and third-party capital, unlocking hundreds of billions (ultimately trillions) in additional sales. The primitives of this network scale across industries and geographies: a universal approach to ERP integrations, modern payment rails, and a live trade-credit bureau to underwrite risk — which we're calling Lighthouse.

The Opportunity: Risk & Capital Products 🚢

Credit is the load-bearing beam of our network. Every time a vendor ships goods before getting paid, someone is taking a risk — today it's the vendor, tomorrow it should be a third party at a fair price. Getting that transfer right is what unlocks the next order of magnitude of sales across the wholesale economy, and the only way to get it right is to underwrite buyers more accurately than anyone else in the industry. Listen to the Visa episode of the Acquired podcast to learn how credit card networks did this for retail trade. 

We think we're uniquely positioned to do this. Through Lighthouse, we're building a live, reciprocal trade-credit bureau: vendors on our network contribute real-time payment behavior on a long tail of SMB buyers that no traditional bureau sees. That data — combined with the bank, ERP, and transaction signals already flowing through Wholesail — is a modeling dataset that doesn't exist anywhere else. The first MLE on this team gets to decide what we build with it.

The problems are real and the stakes are significant. Our models directly shape the terms buyers are offered and the losses Wholesail and our capital partners absorb. There's no established playbook here and no legacy stack to inherit — you'll be setting the direction for how we do modeling, data engineering, and production ML at Wholesail for years to come.

The Role ⚒️

As the founding MLE on the Risk Engineering & Capital Products team, you'll own credit risk modeling end-to-end — from problem framing and data pipeline design, through model development and validation, through production serving and monitoring. You'll work directly with the hiring manager, our engineering and product leadership, and our hands-on advisor Dan Massoni (former Chief Commercial Credit Officer of American Express) to decide what to build, explore features to model and how you’ll scale our approach risk scoring. .

Expect your work to span:

• Modeling. Design, build, and validate credit risk models (PD, LGD, EAD, fraud, exposure sizing, pricing) against our proprietary reciprocal-bureau data and external signals. Choose the right tool for the job — gradient-boosted trees, deep models, LLMs, or classical statistical models — and defend the choice.
• Data engineering. Build the pipelines, feature logic, and training datasets that your models (and future models) run on. At this stage, the MLE owns the full data path; over time we'll grow a team around you.
• Productionization. Ship models into production systems where they make real decisions on real dollars. Own deployment, monitoring, drift detection, and iteration.
• Partnering cross-functionally. Work closely with product, engineering, capital markets, and external lending and insurance partners. Explain your models to people who care about the outcomes but not the math, and translate their constraints back into modeling decisions.
• Shaping the team. Help define the hiring bar and interview process for the data scientists and MLEs who will join after you. You'll have a strong voice in what this team becomes.

Qualifications

• 5+ years of experience building models for production use cases.
• Deep modeling skill: strong command of supervised learning on tabular data, including feature engineering, model selection, hyperparameter tuning, calibration, and rigorous offline and online evaluation.
• Strong data engineering skills — comfortable owning ETL and feature pipelines end-to-end against real, messy production data (SQL and a modern data-processing stack).
• Proficiency in Python and the modern ML ecosystem (pandas, scikit-learn, PyTorch or TensorFlow, XGBoost/LightGBM, Jupyter, etc.).
• Solid statistical reasoning — you can spot leakage, selection bias, label noise, and spurious correlations, and you know why your offline metric doesn't always predict online performance.
• Track record of owning models end-to-end: not just prototyping in a notebook, but taking something from a problem statement to a production system that makes decisions.
• Excellent written and spoken English communication skills; ability to explain model behavior, tradeoffs, and limitations to non-ML audiences (product, ops, capital partners, leadership).
• The spirit of a team player who believes in fostering a healthy and supportive work environment.

Bonus Qualifications

• Direct experience in credit risk modeling — PD/LGD/EAD models, scorecards, underwriting models, exposure management, or collections modeling — particularly for SMB or subprime segments.
• Experience in fintech, lending, payments, fraud, or insurance, or with regulated modeling environments (model risk management, adverse action, fair lending).
• Experience building or operating ML platform components — feature stores, model registries, training orchestration, online serving, monitoring/drift detection.
• Backend engineering depth: comfortable writing production services, designing APIs, and reasoning about distributed systems that consume your models.
• Strong data analysis background — fluency in exploratory analysis, experimentation, and translating data into product and business decisions.
• Experience with LLMs and agentic systems applied to operational problems (document understanding, KYB, entity resolution, agent tooling).
• Experience as the first or founding ML hire at a company, or early on a team that had to build ML infrastructure from scratch.
• Published research, Kaggle placements, or prominent OSS contributions in the ML or data ecosystem.
• BA or BS in Computer Science, Statistics, Mathematics, a related technical field, or equivalent practical experience.
• Advanced degree (MS or PhD) in a quantitative field.

Equal Employment Opportunity

Wholesail is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of race, color, religion, national origin, sex, gender identity or expression, sexual orientation, age, marital status, veteran status, disability status, pregnancy, or any other characteristic protected by federal, state, or local law. Employment decisions at Wholesail are based on qualifications, merit, and business needs. We encourage applicants from all backgrounds to apply.

We are looking for a Senior Staff Software Engineer for Application Engineering. As a Sr Software Engineer on the Applications Engineer team, you’ll be part of a cross-functional team whose mission is to deliver real-world applications and value from the leap forward in computing enabled by quantum technology. 

We don't just build the world's best quantum computers - we apply cutting edge quantum computing tools to real world problems. Until now, our team of Applications Scientists have been building prototypes, working closely with our customers. Our new department, Applications Engineering, will ultimately help transform proven quantum computing use-cases pioneered by our scientists into production applications, making those innovations available to more people and businesses by packaging and deploying these tools as SaaS applications.

But first, we must mature existing proof-of-concept quantum code into production grade applications. As a hands-on team lead for a new engineering team, you will work on enhancing developer experience (DevEx) by applying best practices to research-grade code. You will also set up CI / CD pipelines, software packing tools, and pre/post flight tests for new algorithms as they go into deployment. Where possible you will also be in charge of optimizing and parallelizing traditional algorithms, to reduce pre-processing and post-processing workloads. You will spearhead a new team taking newly invented quantum applications, and turning them into well defined modules of reliable SaaS applications. Think of it as being a forward-deployed engineer for quantum computing applications; the strongest candidates will be generalists with wide-ranging interests. 

This team will work closely with Developer Tools, Infrastructure, and Product teams to solve problems by choosing the right technologies, and standardizing and automating those solutions. The right engineer will be comfortable working on refining chemistry simplification via Quantum Computing tool one quarter, and turning a novel quantum topology solver into a service the next - and love pair programming with scientists. They’ll bring the quantum expertise; you bring the deep and broad experience in classical programming.

Responsibilities:

• Partner with Application scientists to help deliver cutting-edge solutions for customers
• Elevate Applications code quality generally through architectural review, code review, standards, and best practices
• Improve Applications pace of delivery by creating CI / CD pipelines and other packaging automation, as well as test infrastructure and coverage
• Help productize computing code, especially improving data processing in pre-quantum and post-quantum processing steps
• Work closely with peers in Applications, Developer Tools, and Product teams to share best practices and feedback on internal tools
• Reporting to the Director of Application Engineering, help hire and mentor developers on the team

You’d be a good fit with:

• 12+ years of experience as a backend or full-stack developer, preferably in Python or Golang
• 5+ years of experience in setting up and maintaining CI / CD pipelines, in Github Actions, Jenkins, or similar tools
• 5+ years of experience in C/C++ or Rust
• 3+ years of experience in Docker, k8s, or similar containerization technologies
• A track record of shipping containerized applications that solve real business problems
• Previous work in scientific computing, quantum computing, or a related field
• Daily usage of augmented coding tools like Claude or Gemini 

You’d be a great fit with:

• 5+ years of experience in scientific computing, especially with data analysis using numpy, pandas, or similar
• Experience in HPC or performance optimization
• Experience in converting traditional algorithms into CUDA, OpenMP workflows, or similar parallelization experience
• Experience with workflow orchestration engines like Airflow, Dagster, or Temporal

Location: This position can work onsite or hybrid from our office in Bothell, WA; College Park, MD; or Boulder, CO
Travel: 5% for quarterly travel for collaboration 
Job ID:  1312

The approximate base salary range for this position is $192,979 - $252,659. The total compensation package includes base, bonus, and equity.

About Skyloom 

Skyloom, an IonQ company, is a telecommunications company founded with the mission to develop, deploy, and operate one of the fundamental pieces of tomorrow's space communication infrastructure to provide data transport services on a planetary scale. If you have a strong sense of purpose, focus, urgency, and willingness to compete, or if working on space lasers excites you, join our team to develop the future generation of space communication technology.

Skyloom is building the orbital infrastructure for the next renaissance in global connectivity. As Earth’s first space-based telecommunications company, we deliver fiberless, planetary-scale data transport that dramatically increases bandwidth, availability, and resiliency—while reducing latency and total cost of operations.

Our proprietary optical technology enables secure, ultra-high-capacity connections across our end-to-end, multi-orbit network or within your own infrastructure, unlocking a new standard for global communications.

Location: This role will be onsite in our Broomfield, Colorado office. 
Travel: Up to 5%
Job ID:  1450

The Role

We are seeking a V&V and Operations Tools Developer to design, build, and maintain the critical software infrastructure and tooling required for the early design verification, system deliveries validation, and operational monitoring of our flight systems. This role is fundamental to achieving mission assurance by automating testing, supporting complex physical testbeds involving external sensors and actuators, and facilitating early hardware/software integration.

Responsibilities 

• Develop high-quality, robust automation scripts and tools to support early hardware integration testing and interface verification.
• Apply Object- Oriented Programming(OOP) principles to design scalable and reusable software libraries for hardware interface communication and complex data acquisition from the testbed.
• Design and implement automated test sequences for health integration and performance verification of flight systems, analyzing feedback loops, sensor readings, and actuator responsiveness.
• Manage and configure the physical testbed infrastructure, including integrating and calibrating external sensors, actuators, and instrumentation required for comprehensive system testing.
• Develop and maintain the tools and framework necessary to execute Hardware-in-the-Loop (HIL) testing, including environment simulation, real-time data injection, and automated fault insertion.
• Implement minimal services to facilitate data handling, instrument control, and integration between various V&V tools in a distributed physical testbed environment.
• Utilize Docker to containerize development, testing, and V&V environments, ensuring consistency and reproducibility across the organization.
• Administer and maintain Linux operating systems that host the V&V testbeds, simulation platforms, and operational tools.
• Configure and troubleshoot networking protocols and services (TCP/IP, UDP, etc.) to ensure reliable connectivity between test equipment, flight hardware interfaces, and data centers.

Qualifications

• Systemic Mindset: Ability to view the flight system and its V&V testbed not as separate components, but as an integrated system, understanding dependencies, interfaces, and optimizing end-to-end performance.
• Python: Advanced proficiency for tool development and scripting. 
• Strong understanding and application of Object-Oriented Programming (OOP).
• Linux: Experience as user, basic administration for troubleshooting in various distributions.
• Bash scripting for system automation and pipeline execution.
• Experience with Docker for containerizing environments.
• Minimal experience designing or interacting with REST microservice.

Preferred Qualifications

• Experience with Observability Stacks (e.g., ELK/Influx/Grafana) for monitoring testbed health, data logging, and visualizing operational metrics.
• Prior experience in the validation of control software or safety-critical systems.
• Familiarity with data analysis libraries such as Pandas and plotting libraries like Matplotlib for analyzing sensor and performance data.
• Understanding of Distributed Systems architecture and challenges.
• Documentation Automation: Experience integrating documentation tools like Sphinx, Asciidoc or Markdown.

Compensation 

The approximate base salary range for this position is $114,477 - $150,248. The total compensation package includes base, bonus, equity, and a range of benefit options found on our career site.  

**Benefits will be provided by Skyloom, Inc., an IonQ Company, and will differ from the benefits listed on the IonQ website.**

Come Work With Us:

Metropolitan Commercial Bank (“MCB” or the “Bank”) is a New York City–based, full-service commercial bank providing tailored banking solutions to businesses, institutions, and individuals. Founded in 1999, MCB operates banking centers in Manhattan and Boro Park, Brooklyn, within New York City, as well as in Great Neck on Long Island, New York, and Lakewood, New Jersey. The Bank recently expanded to Miami, Florida with their newest Brickell banking center.

Metropolitan Commercial Bank offers a comprehensive suite of commercial, business, and personal banking products and services to small businesses, middle-market and corporate enterprises, private and public institutions, municipalities, and local government entities.

The Bank has earned national recognition for its financial performance, innovation, and strategic growth. The Bank was named one of Newsweek’s Best Regional Banks in 2024 and 2025. Additionally, MCB recently received Editor’s Choice recognition at the Banking Tech Awards USA for Digital Onboarding & Omnichannel Banking and in 2026, the Bank earned Great Place To Work certification and received the Web Award Standard of Excellence for MCBankNY.com.

We are a client-focused organization that values technological innovation and excellence. A strong technical mindset, AI fluency, and adaptive skills are essential for our employees to effectively contribute to our mission and drive our success. We foster human–AI teaming and strong governance to ensure technology is used responsibly and in alignment with Bank policies and procedures. For more information about the Bank, please visit the Bank’s website at MCBankNY.com.

Position Summary:

Metropolitan Commercial Bank (the “Bank”) is seeking a VP-level AI/ML Engineer to deploy AI solutions at enterprise scale, with a strong emphasis on Large Language Model (LLM) applications and modern MLOps & AIOps practices. This role sits at the intersection of data science and software engineering, reporting to the manager of the IT Application Development and Support team and working closely with the Chief Artificial Intelligence Officer, transforming innovative AI prototypes into robust, scalable production systems. The AI/ML Engineer will lead the deployment of high-impact AI capabilities (e.g., generative AI systems, personalization engines, automation tools) and ensure scalable AI platforms that deliver real-world value. The role also includes designing, constructing, and maintaining the Bank’s AIOps solution, with Snowflake as the primary ML platform (e.g., Snowpark Python, UDFs/UDTFs, Tasks/Streams, and Snowflake-native ML).

Standard 4-day in-office requirement, 1 day remote (of your choosing)

Essential Functions & Responsibilities 

Production Architecture & AIOps

• Establish and enforce architecture standards for production AI systems, including data pipelines, model serving infrastructure, and real-time inference services.
• Implement AIOps/MLOps pipelines for CI/CD of ML models, model governance, monitoring, and lifecycle management.
• Design and maintain scalable software applications with integrated AI/ML capabilities.
• Develop software architecture and design patterns to ensure performance and scalability.
• Write clean, maintainable code in general-purpose programming languages (Python, Java, C, C++, Go).
• Implement and manage data pipelines for preprocessing and transforming data for AI/ML models.
• Integrate AI/ML models into production environments and optimize for reliability and scalability.
• Apply Site Reliability Engineering (SRE) principles and implement monitoring and alerting solutions.
• Conduct code reviews and provide technical guidance to junior developers.
• Stay current with advancements in software engineering and AI/ML technologies.
• Adhere to agile and lean software development best practices.
• Thoroughly document all developed models and processes according to relevant policies and standards.
• Support the production environment by either resolving technical or functional issues, in line with the procedures defined by the Bank.

Cross-Functional Collaboration

• Partner with data scientists, AI scientists, product managers, data engineers, DevOps, and business stakeholders to operationalize AI algorithms.
• Mentor or train teams and coordinate between research-oriented AI scientists and engineering teams to continuously improve models with production feedback.

Scaling & Performance

• Ensure AI solutions perform at scale, handling thousands of daily inferences with low latency and high reliability.
• Optimize model serving using techniques like model compression, caching, and hardware acceleration.

Implement robust monitoring and alerting for model performance to detect and address degradation (e.g., drift, latency issues).

Qualifications & Skills:

• 6+ years of experience
• Master’s or PhD in a relevant field (Computer Science, Software Engineering,  Machine Learning, Data Science, Statistics, etc.) is strongly preferred, especially with research or thesis work related to AI/ML
• LLM & GenAI Mastery: Expert in building and deploying LLM-based applications using RAG, prompt engineering, and vector databases. Skilled in LLMOps tools (LangChain, LlamaIndex) and fine-tuning models for enterprise use, including agent-based architectures.
• MLOps & Cloud Infrastructure: Proficient in cloud ML platforms (AWS, GCP, Azure) and MLOps workflows. Uses Docker, Kubernetes, and IaC tools (Terraform, CloudFormation) for scalable deployments. Experienced in CI/CD, real-time inference, GPU optimization, and ML observability (Prometheus, Grafana, MLflow).
• Full-Stack Development: Capable of building end-to-end AI solutions, from front-end (React) to back-end APIs (Flask, FastAPI, Node.js). Skilled in integrating ML models with databases (SQL, NoSQL) and delivering seamless user experiences through robust software engineering.
• Proficient in Python (pandas, scikit-learn), deep learning (PyTorch/TensorFlow/Keras), NLP/LLMs, LangChain, embeddings/vector search, and classic ML.
• Experienced with Snowflake-native ML (Snowpark Python, UDFs/UDTFs, Tasks/Streams).
• Competent in data engineering (SQL, ETL/pipelines, Spark/PySpark) and handling large structured/unstructured datasets.
• Strong understanding of AI/ML algorithms, application architecture, and design patterns.
• Excellent problem-solving, analytical, communication, and collaboration skills.

Preferred Qualifications & Skills

• Financial services domain experience (fraud risk, AML, underwriting, or commercial/treasury analytics).
• Hands-on experience with Snowflake ML/Snowpark (Python), Tasks/Streams, secure external functions, and feature management/registry tooling.
• Familiarity with fairness toolkits, XAI frameworks, and preparing models for validation, audit, or regulatory exams.
• Knowledge of SR 23‑4 (third‑party risk), NYC Local Law 144 (AEDT), NYDFS Part 500 (cyber).
• Ability to work in a constantly evolving environment.
• Excellent written and verbal communication skills.
• Strong listening and teaching abilities.
• Demonstrated analytical, troubleshooting, and problem-solving skills.
• Quick learner of new technologies.
• Self-directed with strong technology and communication skills.
• Ability to synthesize multiple sources of information and understand the broader operational context of the Bank.
• Collaborative team player with a practical and creative approach in a dynamic work environment.
• Ability to handle ambiguity, multitask, and adapt quickly to changing priorities.

Potential Salary: $130,000 - $200,000 annually

This salary range reflects base wages and does not include benefits, bonus, or incentive pay. Salary bands are purposefully wide ranging to encompass the different factors considered in determining where a candidate falls in the range, including but not limited to, seniority, performance, experience, education, and any other legitimate, non-discriminatory factor permitted by law. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed here.

Metropolitan Commercial Bank provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.

This applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.

Come Work With Us:

Metropolitan Commercial Bank (“MCB” or the “Bank”) is a New York City–based, full-service commercial bank providing tailored banking solutions to businesses, institutions, and individuals. Founded in 1999, MCB operates banking centers in Manhattan and Boro Park, Brooklyn, within New York City, as well as in Great Neck on Long Island, New York, and Lakewood, New Jersey. The Bank recently expanded to Miami, Florida with their newest Brickell banking center.

Metropolitan Commercial Bank offers a comprehensive suite of commercial, business, and personal banking products and services to small businesses, middle-market and corporate enterprises, private and public institutions, municipalities, and local government entities.

The Bank has earned national recognition for its financial performance, innovation, and strategic growth. The Bank was named one of Newsweek’s Best Regional Banks in 2024 and 2025. Additionally, MCB recently received Editor’s Choice recognition at the Banking Tech Awards USA for Digital Onboarding & Omnichannel Banking and in 2026, the Bank earned Great Place To Work certification and received the Web Award Standard of Excellence for MCBankNY.com.

We are a client-focused organization that values technological innovation and excellence. A strong technical mindset, AI fluency, and adaptive skills are essential for our employees to effectively contribute to our mission and drive our success. We foster human–AI teaming and strong governance to ensure technology is used responsibly and in alignment with Bank policies and procedures. For more information about the Bank, please visit the Bank’s website at MCBankNY.com.

Position Summary:

Metropolitan Commercial Bank is seeking a VP-level Applied AI & Machine Learning Scientist to design, build, and validate production-grade AI/ML and Generative AI solutions in a highly regulated banking environment. This role focuses on high-impact use cases—fraud detection, AML alert optimization, AI-assisted credit memo generation for underwriting decision support, contact center AI assistant/copilots, and personalization for treasury/commercial clients—delivered with rigorous governance, explainability, fairness testing, privacy-by-design, cybersecurity, and model lifecycle controls aligned to SR 11-7 and MCB’s Trustworthy & Responsible AI Principles. The role emphasizes Snowflake as the primary ML platform (e.g., Snowpark Python, UDFs/UDTFs, Tasks/Streams, and Snowflake-native ML).

Standard 4-day in-office requirement, 1 day remote (of your choosing)

Essential Functions & Responsibilities 

Applied AI/ML development:

• Design and implement models for fraud detection, AML alert scoring/triage, AI-generated credit memo drafting and underwriting decision support, contact center AI assistants, and personalization for commercial/treasury use cases.
• Leverage modern methods: Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), embeddings and vector databases, transformers, boosting, anomaly/outlier detection, and classical ML.
• Embed explainability (e.g., SHAP, interpretable scorecards/monotonic models) and conduct pre-/post-deployment bias testing with documented remediation.

Model validation, documentation & governance (SR 11‑7):

• Produce audit-ready documentation (methodology, assumptions, data lineage, limitations, testing) and register models in the inventory with owners/materiality.
• Facilitate independent validation/effective challenge; obtain required approvals before deployment; maintain change management and periodic review cadence.
• Define monitoring, drift thresholds, retraining triggers, and safe rollback/kill-switch procedures; maintain human-in-the-loop checkpoints for high-impact decisions.

Productionization & MLOps on Snowflake

• Package, deploy, and operate models via CI/CD, containerization, and model registry; instrument KPIs/KRIs and alerting dashboards. Operate models natively on Snowflake using Snowpark Python, UDFs/UDTFs, Tasks/Streams, and secure external access where required.
• Partner with Engineering to integrate models via secure APIs/batch; ensure scalability, resiliency, and observability in cloud/on‑prem (e.g., Snowflake, Azure ML, Databricks).

Regulatory, privacy, and cybersecurity alignment:

• Design for ECOA/Reg B (adverse action specificity), UDAAP, FCRA, GLBA privacy, and NYDFS 23 NYCRR 500 cybersecurity requirements.
• Apply privacy-by-design (data minimization, purpose limitation, retention), strong access controls/segregation, and secure SDLC/red teaming for GenAI stacks.

Third‑party AI & data stewardship:

• Support due diligence, testing, and ongoing monitoring of vendor AI/data providers per SR 23‑4; evaluate conceptual soundness, fairness, and security.
• Negotiate/verify contractual controls (no vendor training on MCB/NPI, subprocessors disclosure, audit rights, exit/portability).
• Ensure AEDT compliance (NYC Local Law 144) for any HR-related AI tools.

Cross‑functional partnership:

• Collaborate with Model Risk, Compliance/Legal, Cyber/IT, Data Privacy, Internal Audit, and business owners to meet objectives while staying within risk appetite.
• Communicate complex results, risks, and limitations clearly to technical and non‑technical stakeholders (management committees, examiners).

Innovation, coaching, and best practices:

• Evaluate emerging ML/GenAI methods, LLM evaluation techniques, Snowflake‑native capabilities (e.g., vector search, orchestration), and governance tooling; lead POCs within established control gates.
• Mentor junior staff; promote responsible AI practices, documentation standards, and reproducibility.

Qualifications & Skills:

• 6+ years of relevant work experience.
• Expertise in Python (pandas, scikit‑learn), deep learning (PyTorch/TensorFlow), NLP/LLMs, LangChain, embeddings/vector search, and classic ML.
• MLOps proficiency with CI/CD, containerization (Docker), registries, and observability; cloud ML (Snowflakes-native ML, Azure ML or Databricks preferred).
• Snowflake‑native ML proficiency: Snowpark Python, UDFs/UDTFs, Tasks/Streams; ability to build and operate ML workflows inside Snowflake.
• Data engineering competency (SQL, ETL/pipelines, Spark/PySpark); ability to work with structured/unstructured data.
• Explainability (e.g., SHAP) and fairness testing; ability to produce interpretable reason codes for ECOA/Reg B adverse actions as applicable.
• Strong grasp of SR 11‑7 lifecycle, model documentation, and operational monitoring within three lines of defense governance.
• Excellent communication; ability to translate technical detail to business/risk stakeholders and drive decisions.
• Curiosity and problem‑solving mindset; ability to balance innovation with disciplined risk management.

Preferred Qualifications & Skills

• Master’s or PhD in a relevant field (Computer Science, Machine Learning, Data Science, Statistics, etc.) is strongly preferred, especially with research or thesis work related to AI/ML, NLP, or model interpretability.
• Financial services domain experience (fraud risk, AML, underwriting, or commercial/treasury analytics).
• Hands-on with Snowflake ML/Snowpark (Python), Tasks/Streams, secure external functions; experience with feature management/registry tooling a plus. model registry and pipeline orchestration; Kubernetes a plus.
• RAG architectures, vector databases, prompt engineering, and LLM evaluation (accuracy, hallucination, safety).
• Fairness toolkits and XAI frameworks; experience preparing models for validation, audit, or regulatory exam discussions.
• Familiarity with SR 23‑4 (third‑party risk), NYC Local Law 144 (AEDT), NYDFS Part 500 (cyber).
• Ability to work in a constantly evolving environment
• Must have excellent written and verbal communication skills
• Must be a good listener and good teacher
• Demonstrate analytical, troubleshooting and problem-solving skills
• The ability to learn new technologies quickly
• Self-directed individual with technology and communication skills.
• Ability to take in multiple sources of information with an understanding of the bigger picture need, want, and operation of the Bank.
• Collaborative team-player who can find creative and practical solutions in a dynamic work environment.
• Ability to handle ambiguity, juggle multiple matters at once, and quickly and seamlessly shift from one situation or task to another.

Potential Salary: $130,000 - $200,000 annually

This salary range reflects base wages and does not include benefits, bonus, or incentive pay. Salary bands are purposefully wide ranging to encompass the different factors considered in determining where a candidate falls in the range, including but not limited to, seniority, performance, experience, education, and any other legitimate, non-discriminatory factor permitted by law. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed here.

Metropolitan Commercial Bank provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.

This applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.

About the Role Scale is at the frontier of GenAI and human-AI collaboration. The Gen AI Ops Trust and Safety team is focused on safeguarding human authenticity and genuineness in AI training. We are looking for a highly analytical Technical Program Manager (TPM) who leans heavily into fraud analytics and data-driven strategy to protect our ecosystem.

This isn't a project management role. You will act as the lead investigative analyst and program owner for our fraud defense portfolio. Your day-to-day will involve diving deep into complex datasets to uncover hidden fraud vectors, and then translating those analytical insights into scalable rules, policies, and operational programs. By utilizing AI coding tools at high velocity, you will build out analytics pipelines, dashboards, and detection logic to shift Trust and Safety from a reactive function to a strategic one that balances safety and growth.

You will:

• Analyze large, messy behavioral events to identify ambiguous and constantly evolving fraud patterns across the contributor lifecycle.
• Translate your analytical findings into actionable detection logic. You will redesign rules, optimize thresholds, and decision flows to catch bad actors while minimizing friction for high-quality contributors.
• Establish robust KPIs, build tracking dashboards, and define offline evaluation frameworks (e.g., false positive monitoring, precision/recall analysis) to continuously measure the health of our risk strategy.
• Act as the connective tissue between data, operations, and engineering. You will use your analytical findings to implement technical execution, taking new detection capabilities from data prototype to production deployment.
• Leverage AI-assisted IDEs daily to rapidly write complex SQL queries, automate data pulls, and streamline your analytical workflows.
• Connect signals, data, and operations to see the full picture. Provide clear, direct communication regarding fraud trends and strategy shifts to both technical and non-technical partners.

Ideally, you’d have:

• 5-8 years of experience in risk strategy and fraud analytics. You have a battle-tested track record of reverse-engineering adversarial patterns, dismantling complex fraud vectors, and driving highly analytical Trust & Safety or TPM programs.
• Expert proficiency in SQL skills. You must be highly comfortable extracting insights from large datasets in noisy, adversarial environments.
• An execution-driven mindset focused on delivering measurable results, not just theoretical analysis. You are comfortable working in ambiguity and taking ownership from data problems to operational solutions.
• Strong proficiency with AI coding assistants to accelerate data exploration and query writing.
• Deep understanding of how to balance aggressive fraud detection with marketplace growth. You make decisions based on what's right for the business, not what's convenient.

Nice to haves:

• Solid Python skills (e.g., Pandas, NumPy) for advanced data manipulation and scripting
• Experience working in marketplace or gig-economy platforms is a plus

AI is becoming vitally important in every function of our society. At Scale, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including: generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re accelerating the usage of frontier data and models by building complex agents for enterprises around the world through our Scale Generative Platform (SGP).

The SGP ML team works on the front lines of this AI revolution. We interface directly with clients to build cutting edge products using the arsenal of proprietary research and resources developed at Scale. As an Applied AI Engineer, you’ll work with clients to create ML solutions to satisfy their business needs. Your work will range from building next-generation AI cybersecurity firewalls to creating transformative AI experiences in journalism to applying foundation genomic models making predictions about life-saving drug proteins. Daily data-driven experiments will provide key insights around model strengths and inefficiencies which you’ll use to improve your product’s performance. If you are excited about shaping the future of the modern AI movement, we would love to hear from you!

You will:

• Own, plan, and optimize the AI behind our Enterprise customer’s deepest technical problems
• Leverage SGP to build the most advanced AI agents across the industry including multimodal functionality, tool-calling, and more
• Have experience gathering business requirements and translating them into technical solutions 
• Meet regularly with customer teams onsite and virtually, collaborating cross-functionally with all teams responsible for their data and ML needs
• Push production code in multiple development environments, writing and debugging code directly in both our customer’s and Scale’s codebases.
• Be able and willing to multi-task and learn new technologies quickly

Ideally you'd have:

• 7+ years of full-time engineering experience, post-graduation
• A love for solving deeply complex technical problems with ambiguity using state of the art research and AI to accomplish your client’s business goals
• Strong engineering background: a Bachelor’s degree in Computer Science, Mathematics, or another quantitative field or equivalent strong engineering background.
• Deep familiarity with a data-driven approach when iterating on machine learning models and how changes in datasets can influence model results
• Experience working with cloud technology stack (eg. AWS or GCP) and developing machine learning models in a cloud environment
• Proficiency in Python to write, test and debug code using common libraries (ie numpy, pandas)

Nice to haves:

• Strong knowledge of software engineering best practices
• Have built applications taking advantage of Generative AI in real, production use cases
• Familiarity with state of the art LLMs and their strengths/weaknesses

Visa sponsorship is not offered for this role.
Learn More about Value Engineering at Celonis Here

Role Description

• As a Senior Value Engineering Consultant specializing in the Energy Sector, you are pushing the envelope in solving business-critical problems for our strategic customers across utilities, oil & gas, and renewable energy. You will be working with our most strategic energy clients, understanding their unique objectives—from grid modernization and asset lifecycle management to navigating the energy transition—and building Celonis solutions using the world’s leading Process Intelligence (PI) platform in combination with the largest AI and ML technology partners, such as Microsoft, OpenAI, and Databricks.
  
  With Celonis’ Process Intelligence (PI) platform, we feed operational context to AI so it understands our customers’ complex business realities (bridging IT and OT systems) and enables them to industrialize AI, unlocking real ROI on deployments at scale. You will prototype these solutions, demonstrate their value to Energy Executives, and ensure successful implementation, adoption, and value realization in order to increase the footprint of Celonis across the global energy landscape.

Key Responsibilities:

• AI Discovery & Solutioning: Understand energy customers' AI strategy and sector-specific challenges (e.g., predictive maintenance, outage management, supply chain resilience, or regulatory compliance). As a Celonis product and energy domain expert, find the best problem-solution fit and translate customer requirements into innovative solutions that move the needle.

• Pre- and Post-Sales Execution: Actively drive the full customer lifecycle. Lead technical discovery and capability demonstrations during the pre-sales cycle, and remain deeply involved post-sale to guide implementation, ensuring agreed value and adoption thresholds are successfully reached.

• Hackathons & Prototyping: Think out of the box, have a „can-do“ attitude, and don’t shy away from complex operational problems. Leverage cutting-edge AI technologies to rapidly build creative prototypes in customer hackathons, solving critical pain points specific to power generation, transmission, and O&G operations.

• Agentic Process Transformation: Support our customers in achieving real ROI out of AI deployments at scale, enabling a fundamental shift from traditional, rule-based automation to the use of autonomous AI agents empowered by our Celonis Process Intelligence Platform (e.g., intelligent field service routing or autonomous procurement).

• Proof Projects: End-to-end execution of business-critical Proof-of-Value projects. This includes architecting and delivering secure, scalable LLM/agent systems with RAG, tools, and guardrails, while seamlessly integrating with enterprise data, identity protocols, and stringent energy compliance frameworks.

• Domain & Industry Leadership: Serve as the internal and external technical subject matter expert for the Energy vertical, scaling knowledge across the organization regarding utilities, upstream/downstream oil & gas, and renewable energy processes.

Requirements:

• 5+ years of experience leading technical pre-sales and post-sales engagements specifically within the Energy sector (Utilities, Oil & Gas, and/or Renewables). This includes defining AI roadmaps, building compelling ROI/TCO business cases, and guiding technical implementations through to value realization.

• Deep understanding of business processes native to the Energy sector (such as Asset Management, Field Services, Supply Chain, Capital Projects, or Trading) with the ability to translate high-level business needs into specific, impactful AI use cases.

• Expertise in generative AI techniques like RAG, few-shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning used to build high-impact use cases (e.g., automated processing of engineering documents, regulatory filings, or intelligent diagnostic chatbots).

• Solid knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and technologies relevant to handling large-scale industrial data.

• Strong presentation skills to both internal and external stakeholders (including C-level executives and operational leaders), whether leading technical whiteboarding sessions or formal readouts and demos.

• Bachelor’s Degree required; Master's Degree in computer science, engineering, mathematics, or related fields, or equivalent work experience preferred.

Nice to have (big plus):

• Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, while ensuring safety through rigorous evaluations and guardrails suited for highly regulated industries.

• Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.

• Experience in deploying and monitoring models at scale across major cloud platforms (AWS Bedrock, Azure AI, GCP Vertex) and familiarity with IT/OT convergence and industrial IoT data structures.

Visa sponsorship is not offered for this role.
Learn More about Value Engineering at Celonis Here

Role Description

As a Senior Value Engineer specializing in the Public Sector, you are pushing the envelope in solving critical operational challenges for State and Local Government agencies. You will be working with our most strategic public sector clients, understanding their unique objectives—from enhancing citizen services and modernizing legacy systems to optimizing grant management and regulatory compliance—and building Celonis solutions using the world’s leading Process Intelligence (PI) platform in combination with top AI and ML technology partners, such as Microsoft, OpenAI, and Databricks.

With Celonis’ Process Intelligence (PI) platform, we feed operational context to AI so it understands the complex realities of government operations (breaking down agency silos) and enables agencies to industrialize AI. This unlocks real ROI on AI deployments, maximizing taxpayer value at scale. There is no AI without PI. You will prototype these solutions, demonstrate their value to government CIOs and Agency Directors, and ensure successful implementation, adoption, and value realization to increase the footprint of Celonis across state and local governments.

Key Responsibilities:

• AI Discovery & Solutioning: Understand public sector AI strategies and specific agency challenges (e.g., benefits administration backlogs, procurement bottlenecks, public safety resource allocation, or permitting delays). As a Celonis product and government domain expert, find the best problem-solution fit and translate agency requirements into innovative solutions that deliver measurable public impact.

• Pre- and Post-Sales Execution: Actively drive the full customer lifecycle within the public sector. Lead technical discovery and capability demonstrations during the complex government pre-sales and procurement (RFP) cycles, and remain deeply involved post-sale to guide implementation, ensuring agreed value and adoption thresholds are successfully met.

• Hackathons & Prototyping: Think out of the box, have a „can-do“ attitude, and don’t shy away from complex legacy processes. Leverage cutting-edge AI technologies to rapidly build creative prototypes in agency hackathons, solving critical pain points to improve constituent experiences.

• Agentic Process Transformation: Support our government customers in achieving real value out of AI deployments at scale, enabling a fundamental shift from traditional, rigid, paper-heavy workflows to the use of autonomous AI agents empowered by our Celonis Process Intelligence Platform (e.g., intelligent case triaging or automated compliance checks).

• Proof Projects: End-to-end execution of critical Proof-of-Value projects. This includes architecting and delivering secure, scalable LLM/agent systems with RAG, tools, and guardrails, while seamlessly integrating with government enterprise data, identity protocols, and stringent compliance/security frameworks (e.g., StateRAMP, HIPAA, CJIS).

• Domain & Industry Leadership: Serve as the internal and external technical subject matter expert for the State & Local Government vertical, scaling knowledge across the organization regarding agency processes and public sector nuances.

Requirements:

• 5+ years of experience leading technical pre-sales and post-sales engagements specifically within the Public Sector (State & Local Government). This includes navigating government procurement cycles, building compelling ROI business cases for public funds, and guiding technical implementations through to constituent value realization.

• Deep understanding of business processes native to state and local governments (such as Health & Human Services, Procurement, Finance, DMV operations, or Public Safety) with the ability to translate high-level policy or agency needs into specific, impactful AI use cases.

• Expertise in generative AI techniques like RAG, few-shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning used to build high-impact use cases (e.g., intelligent constituent-facing chatbots, automated processing of policy documents, or grant application analysis).

• Solid knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and technologies.

• Strong presentation skills to both internal and external stakeholders (including elected officials, agency directors, and IT leadership), whether leading technical whiteboarding sessions or formal readouts and demos.

• Bachelor’s Degree required; Master's Degree in computer science, engineering, mathematics, or related fields, or equivalent work experience preferred.

Nice to have (big plus):

• Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, while ensuring safety through rigorous evaluations and guardrails suited for highly scrutinized public sector environments.

• Experience in deploying and monitoring models at scale within government-compliant cloud environments (e.g., AWS GovCloud, Azure Government).

• Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.

Visa sponsorship is not offered for this role.
Learn More about Value Engineering at Celonis Here

Role Description

• As an Senior Value Engineer specializing in CPG and Fleet Logistics, you are pushing the envelope in solving business-critical problems for some of the world's most recognized consumer brands and global mobility networks. You will be working intimately with our most strategic enterprise clients, understanding their uniquely complex objectives—spanning from high-velocity beverage manufacturing and Direct Store Delivery (DSD) optimization to massive-scale fleet lifecycle management and dynamic asset routing. You will build Celonis solutions using the world’s leading Process Intelligence (PI) platform in combination with top AI and ML technology partners (e.g., Microsoft, OpenAI, Databricks).

  With Celonis’ Process Intelligence (PI) platform, we feed operational context to AI so it understands the intricate realities of our customers' massive supply chains, vehicle fleets, and distribution networks. This enables them to industrialize AI, unlocking real ROI on deployments at scale—ensuring products are always on the shelf and vehicles are always ready for the customer. There is no AI without PI. You will prototype these solutions, demonstrate their value to global Chief Supply Chain Officers (CSCOs) and Fleet Operations Executives, and ensure successful implementation, adoption, and value realization to solidify and expand the footprint of Celonis across these premier global networks.

Key Responsibilities:

• AI Discovery & Solutioning: Understand the clients' overarching AI strategies and the distinct supply chain challenges inherent to high-volume CPG and fleet mobility (e.g., predictive demand forecasting, route optimization, fleet predictive maintenance, dynamic asset allocation, and mitigating raw material volatility). As a Celonis product and logistics domain expert, translate these complex, physical-world challenges into innovative AI solutions that drive measurable impact.

• Pre- and Post-Sales Execution: Actively drive the full customer lifecycle. Lead technical discovery and capability demonstrations during the expansion/pre-sales cycle within various manufacturing, distribution, and fleet management business units. Remain deeply involved post-sale to guide implementation, ensuring agreed value, efficiency, and adoption thresholds are successfully reached.

• Hackathons & Prototyping: Think out of the box with a "can-do" attitude, tackling heavily siloed legacy logistics and telematics networks. Leverage cutting-edge AI technologies to rapidly build creative prototypes in client hackathons, solving critical pain points across inventory allocation, fleet routing, and DSD execution.

• Agentic Process Transformation: Support these enterprise clients in achieving tangible ROI from AI at scale. Enable a fundamental shift from traditional, rule-based automation to autonomous AI agents empowered by the Celonis Process Intelligence Platform (e.g., autonomous fleet rebalancing, intelligent shipment exception handling, or automated trade promotion deductions).

• Proof Projects: End-to-end execution of business-critical Proof-of-Value projects. Architect and deliver secure, scalable LLM/agent systems with RAG, tools, and guardrails, ensuring seamless integration with complex enterprise ERPs (e.g., SAP), Transportation Management Systems (TMS), Warehouse Management Systems (WMS), and massive IoT/telematics data streams.

• Domain & Industry Leadership: Serve as the internal and external technical subject matter expert for CPG and Mobility Supply Chains, scaling knowledge across the organization regarding the nuances of fast-moving consumer goods and asset-heavy fleet operations.

Requirements:

• 5+ years of experience leading technical pre-sales and post-sales engagements specifically within highly complex Supply Chain, CPG/Beverage, or Fleet Logistics environments. This includes defining AI roadmaps, building compelling ROI/TCO business cases for large-scale distribution networks, and guiding technical implementations through to value realization.

• Deep understanding of supply chain business processes native to high-volume manufacturing and mobility (such as Direct Store Delivery (DSD), Order-to-Cash, Procure-to-Pay, Fleet Lifecycle Management, or Sales & Operations Planning) with the ability to translate high-level operational needs into specific AI use cases.

• Expertise in generative AI techniques like RAG, few-shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning used to build high-impact use cases (e.g., intelligent dispatch assistants, or automated extraction of data from complex vendor contracts and logistics documentation).

• Solid knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and technologies for handling massive, high-velocity transactional and IoT datasets.

• Strong presentation skills to both internal and external stakeholders (including supply chain executives, logistics directors, and enterprise IT leaders), whether leading technical whiteboarding sessions or formal readouts and demos.

• Bachelor’s Degree required; Master's Degree in computer science, supply chain management, logistics, engineering, mathematics, or related fields, or equivalent work experience preferred.

Nice to have (big plus):

• Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, while ensuring safety through rigorous evaluations suited for enterprise-grade supply chain environments.

• Familiarity with CPG and fleet-specific systems (e.g., SAP APO/IBP, Blue Yonder, Manhattan, or major telematics/fleet tracking platforms).

• Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.

• Experience in deploying and monitoring models at scale across major cloud platforms (AWS Bedrock, Azure AI, GCP Vertex).

Visa sponsorship is not offered for this role.
Learn More about Value Engineering at Celonis Here

Your Impact on our Mission

Zocdoc’s most important asset is our people. As a Staff Data Scientist, Marketplace, you’ll play a meaningful role in transforming healthcare by shaping how millions of patients discover and book care.

The Marketplace team is responsible for driving patient demand, optimizing the search and booking experience, and ensuring a healthy, high-performing two-sided marketplace. This includes areas such as Growth, Search, Insurance, and Patient Home.

As a strategic technical leader on the Marketplace Analytics team, you’ll design and scale analytical frameworks that inform product and growth strategy, guide experimentation, and optimize performance across the patient funnel—from acquisition through booking and retention. Your work will directly improve marketplace liquidity, efficiency, and long-term value.

You’ll enjoy this role if you are…

• Passionate about transforming healthcare through data and turning complex datasets into meaningful product insights

• A force multiplier who loves mentoring and enabling teams to deliver scalable, high-impact work

• Comfortable operating at the intersection of business strategy, data science, and product innovation

• Energized by complex, ambiguous problems and skilled at translating them into structured analytical solutions

• Personally motivated by building systems, frameworks, and capabilities that outlast any one analysis

• Serious about your work, but not about yourself — you value rigor and humility equally

Your day to day is…

• Partnering with Product, Marketing, Finance, Engineering, and Commercial teams to translate insights into actionable product and business strategy

• Architecting and executing experimentation frameworks, including A/B testing design, power analysis, variance reduction, and advanced causal inference methodologies (e.g., DiD, synthetic controls, propensity modeling, causal forests)

• Identifying, designing, and guiding the development of analytical models (e.g., LTV, survival, uplift models) to improve provider utilization, acquisition, retention, patient engagement, and client ROI

• Establishing scalable statistical frameworks and best practices to quantify the impact of product and commercial initiatives

• Partnering with Engineering to ensure data quality, event instrumentation, feature availability, and scalable pipelines that enable trustworthy analysis

• Mentoring and upskilling analysts and data scientists, raising the bar for analytical rigor and impact across Zocdoc

• Advancing AI-assisted analytical workflows to streamline recurring analysis and decision support

• Working with cutting edge GenAI tools and technology

You’ll be successful in this role if you have…

• An unrelenting desire to build more equitable, inclusive, and diverse workplaces, and view this role as an opportunity to create positive change

• 8+ years of experience in data science, machine learning, or related quantitative roles

• Proven success developing LTV models, survival models, uplift models, or similar predictive frameworks for customer targeting and optimization

• Deep expertise in SQL and Python (pandas, NumPy, scikit-learn, statsmodels)

• Strong knowledge of experimentation and causal inference, including statistical power analysis, experiment design, and quasi-experimental methodologies

• Experience shaping and scaling analytics functions—not just building individual models

• Experience working in cross-functional product organizations

• The ability to integrate generative AI tools into daily workflows to automate tasks, foster innovation, and maximize productivity

• Superb communication skills and the ability to influence non-technical stakeholders

• The mentality of an entrepreneur/owner with a strong bias to action

• Humility — you treat all people with dignity and respect, regardless of title or tenure

Nice to have

• Master’s or PhD in Statistics, Economics, Applied Math, or another quantitative field

• Experience within a SaaS organization

Benefits

• Flexible, hybrid work environment at our convenient Soho location

• Unlimited Vacation

• 100% paid employee health benefit options (including medical, dental, and vision)

• Commuter Benefits

• 401(k) with employer funded match

• Corporate wellness program with Wellhub

• Sabbatical leave (for employees with 5+ years of service)

• Competitive paid parental leave and fertility/family planning reimbursement

• Cell phone reimbursement

• Catered lunch everyday along with beverages and snacks

• Employee Resource Groups and ZocClubs to promote shared community and belonging

• Great Place to Work Certified

Your Core Responsibilities

• Collaborate with Traders to evaluate and enhance the performance of IMC’s existing proprietary trading models using advanced mathematical and analytical methods.
• Design quantitative methods to gather and analyze historical market data, trading statistics or new data sources and use the result of that research as the basis for new trading strategies.
• Translate trading strategies into mathematical models, algorithms, and other complex computational programs to realize the combined vision of the trading team.
• Build custom computational data analysis tools to explore new quantitative research ideas; design mathematical models to test, visualize and refine trading ideas before implementation.
• Apply rigorous analytical and quantitative techniques to solve complex problems presented by new and existing proprietary trading strategies.
• Coordinate with developers to implement new and improved quantitative trading models and launch them into production on IMC’s proprietary trading platform
• Conduct all quantitative trading research, development, and implementations according to internal best practices to keep IMC’s trading ecosystem logical and consistent.

Partial telecommuting permitted. Multiple positions available.

Quantitative Researcher (Requisition ID: 4453347101)

Your Skills and Experience

This position requires a PhD, or foreign equivalent, in Finance, Financial Mathematics, Financial Engineering, Statistics, Computational Mathematics or similarly quantitative and mathematical field.

Must have demonstrated experience in each of the following skill(s):

• Application of analytical and quantitative techniques to solve complex problems;
• Designing and implementing mathematical models and algorithms, including linear modeling, convex optimization, and numerical methods for large-scale data and parameter estimation;
• Developing and using computational data analysis tools for research;
• Statistical programming techniques (including Python or C++) and designing and executing independent quantitative research projects, including formulation of hypotheses, development of models, empirical testing, and clear documentation of results;
• Time series analysis, applied statistics, and theoretical mathematics (such as probability, measure theory/real analysis, and linear algebra);
• Implementing algorithms, conducting data analysis, and building research pipelines using numerical and scientific computing libraries (such as NumPy, pandas, SciPy) and version control (such as Git);
• Experience with time series modeling and forecasting, including stochastic processes and state-space or related frameworks, such as applied to real-world data; and
• Conducting literature reviews and replicating and extending prior work with methodological rigor and reproducibility.

#LI-DNI

The Role

Fusion is a global electronics distributor building a modern data platform on top of a 20 year old legacy system. Palantir Foundry is a core part of that stack. We use it to deliver unified views of our customers, products, and operations, and we are leaning hard into AIP and agent driven workflows to compress delivery cycles. We are looking for a senior engineer who can work hands on in Foundry: building pipelines, ontology models, agents, and applications alongside our external Palantir consultants today, and progressively taking ownership as the platform matures.

This is an execution focused role with real autonomy. You will design and build solutions, work directly with business stakeholders, and have a voice in how the platform evolves.

What You’ll Do

• Build and maintain data pipelines: Design, build, and optimize batch and streaming pipelines in Foundry (Pipeline Builder with AI assist, Code Repositories, Code Workbook) that ingest data from internal and external source systems.
• Develop ontology models: Create and maintain ontology objects, relationships, link types, functions, and actions that represent business domains and power downstream applications, agents, and AIP workflows.
• Build AI native experiences with AIP: Use AIP Logic to compose AI driven functions, AIP Agent Studio to stand up domain agents, and AIP Threads or AIP Assist to embed conversational interfaces into Workshop apps. Use AIP Evals to validate prompt and agent behavior before promotion to production.
• Leverage AIP Analyst and FDE AI for delivery acceleration: Use AIP Analyst for ad hoc analytical work directly against the ontology, and lean on FDE AI and Solution Designer to scaffold ontology objects, pipelines, and starter applications. Refine the AI generated output, harden it for production, and bring it under proper change control.
• Deliver Foundry applications: Build and iterate on Workshop, Quiver, and Contour applications that give sales, operations, and leadership actionable views of the business, including embedded agentic actions and write back through Ontology Actions.
• Work alongside external consultants: Collaborate closely with our Palantir implementation partners to understand existing designs, validate data flows, and progressively take on more of the day to day Foundry work.
• Connect Foundry to the broader data platform: As the data modernization team builds out medallion layer domain stores and DBT based pipelines on Azure, help align Foundry’s ontology and ingestion paths so the platform can transition from legacy sources to the new architecture cleanly.
• Integrate with external systems: Build and maintain OSDK based integrations and APIs that connect Foundry to internal services and third party platforms.
• Engage stakeholders directly: Gather requirements from business teams, demo solutions, train end users, and incorporate feedback into iterations.
• Ensure data quality and observability: Implement validation, lineage tracking, health checks, and monitoring within Foundry pipelines. Build automated data quality checks, including AIP assisted ones, to keep data accurate and reliable.
• Apply AI tooling to your own workflow: Use AIP and AI assisted development practices to accelerate pipeline work, code review, and documentation.

What You Bring

Data Engineering Foundations

These are the bedrock skills we expect every senior data engineer on this team to have, regardless of platform. Foundry is the surface; these are the muscles underneath.

• 7+ years building production data pipelines in modern stacks.
• Strong SQL, including window functions, CTEs, set operations, and query optimization on large datasets. You can read an execution plan and explain the cost.
• Solid grasp of dimensional modeling (Kimball, star schema), Data Vault, or equivalent. You can defend a modeling choice with reasoning, not dogma.
• Experience designing for slowly changing dimensions, idempotent loads, late arriving data, backfills, and incremental processing.
• Proficiency in Python, including PySpark and pandas for transformation work.
• Working knowledge of the Spark execution model: partitioning, shuffles, skew, broadcast joins, and tuning.
• Experience with ETL and ELT patterns, CDC pipelines, and workflow orchestration.
• Familiarity with modern transformation frameworks such as DBT, including tests, documentation, snapshots, and incremental materializations.
• Understanding of columnar formats (Parquet, Delta, Iceberg), table formats, and partitioning strategies.
• Practical experience implementing data quality frameworks, data contracts, and SLAs between producers and consumers.
• Comfort with Git workflows, code review, branching strategies, and CI/CD for data platforms.

Palantir Foundry and AIP

• 2+ years hands on Palantir Foundry experience with delivered work you can describe in detail.
• Strong knowledge of core Foundry capabilities: Ontology Manager, Ontology Functions and Actions, Pipeline Builder, Code Repositories, Code Workbook, Workshop, Builds, and platform security (branching, resource allocation, rollbacks).
• Experience with Foundry analytical and integration tools: Quiver, Contour, OSDK, and Marketplace products.
• Hands on experience with AIP for augmenting data workflows: AIP Logic, AIP Assist and Threads, and AIP assisted Pipeline Builder.
• Familiarity with newer AIP surfaces is a strong plus: AIP Agent Studio, AIP Analyst, FDE AI and Solution Designer, AIP Evals, and Automate.
• Understanding of how to evaluate, guardrail, and version AI driven functions and agents in a production context.

Cloud, Architecture, and Stakeholder Skills

• Proficiency with Azure data services, or equivalent depth in AWS or GCP with willingness to work in Azure.
• Solid understanding of data modeling across relational, analytical, and document paradigms.
• Ability to translate business requirements into technical designs and explain trade offs to non technical stakeholders.
• Self directed work style. You identify what needs doing and get it done without waiting for direction.

Nice to Have

• Production experience with DBT on Azure (Synapse, Fabric, or Databricks).
• Experience with medallion architecture (Bronze, Silver, Gold) or lakehouse patterns on Databricks, Snowflake, or equivalent.
• Experience evaluating LLM powered features: prompt design, eval harnesses, hallucination guardrails, and cost or latency tuning.
• Experience building or operating real time pricing, recommendation, or scoring engines.
• Familiarity with electronics distribution, supply chain, or ERP data, particularly Dynamics GP and Great Plains.
• Background in domain driven design or microservices architecture.
• TypeScript or C# for OSDK consumers and downstream services.
• Exposure to CRM, payment, or logistics API integrations.

Why Fusion

• Greenfield meets legacy: You’ll help build a modern data platform while keeping a 20 year business running. Real engineering problems, not toy projects.
• Real impact, small team: A lean team where your work is visible and your judgment matters. No layers of approval.
• AI first engineering culture: We use AIP and AI tooling aggressively. One engineer with the right tools does the work of three.
• Global scale: Fusion operates across the US, Singapore, and Hong Kong. The platform serves users worldwide.

Role Description

• As an Applied AI Engineer you are pushing the envelope in solving business-critical problems for our customers. You will be working with our most strategic customers, understanding their objectives and key challenges, and building Celonis solutions using the world’s leading Process Intelligence (PI) platform in combination with the largest AI and ML technology partners, such as Microsoft, OpenAI and Databricks. With Celonis’ Process Intelligence (PI) platform we feed operational context to AI so it understands our customers’ business and enables them to industrialize AI unlocking real ROI on AI deployments and at scale. There is no AI without PI. You will prototype these solutions, demonstrate their value to Executives and ensure successful implementation, adoption and value realization in order to increase the footprint of Celonis at those customers.

Key Responsibilities:

• AI Discovery & Solutioning: Understand customers AI strategy and business critical challenges. As Celonis product & domain expert, find the best problem-solution fit and translate customer requirements into innovative solutions that move the needle
• Hackathons & Prototyping: Think out of the box, have a „can-do“ attitude and don’t shy away from complex problems. Leverage cutting edge AI technologies to rapidly build creative prototypes in customer hackathons solving business critical problems
• Agentic Process Transformation: Support our customers in achieving real ROI out of AI deployments at scale enabling a fundamental shift in business operations from traditional, rule-based automation to the use of autonomous AI agents empowered by our Celonis Process Intelligence Platform
• Proof projects: End-to-end execution of business-critical Proof-of-Value projects, incl. architecture and deliver secure, scalable LLM/agent systems with RAG, tools, and guardrails; integrating with enterprise data, identity, and compliance frameworks.
• Ensure Successful Project Outcome: Applied AI Engineers stay involved with projects until agreed value & adoption thresholds are reached
• Specialize in Domains or Industries: To scale knowledge across the organization Applied AI Engineers specialize in domains (e.g. supply chain) and industries

Requirements:

• 7+ years of experience leading technical pre-sales, including defining AI roadmaps, building compelling ROI/TCO business cases and prototyping of machine learning and generative AI solutions.
• Understanding of generative AI techniques like RAG, few shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning that are used to build high-impact use cases like intelligent chatbots and automated text processors.
• Understanding of business processes across sectors (such as Supply Chain or Finance) with the ability to translate high-level business needs into specific AI use cases.
• Good knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and technologies.
• Strong presentation skills to both internal and external stakeholders (including executives), whether whiteboarding sessions or formal readouts and demos.
• Bachelor’s Degree required, Masters Degree in computer science, engineering, mathematics or related fields, or equivalent work experience preferred.

Nice to have (big plus):

• Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, while ensuring safety through rigorous evaluations and guardrails.
• Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.
• Experience in deploying and monitoring models at scale across major cloud platforms (AWS Bedrock, Azure AI, GCP Vertex)

SLSQ427R253

As a Partner Solutions Architect for Europe, you will work with the technical and sales team members who work directly with our customers. You will develop 'technical champions’ within our top Partners, providing enablement on technical matters related to the Databricks product. Working with our partners, you will help our customers achieve tangible, data-driven outcomes through our Databricks Data Intelligence Platform, enabling data teams to complete projects and integrate them into their enterprise Ecosystem. As a member of our team, you will exercise and develop expertise in those areas, using open source projects such as Apache Spark™, MLflow, and Delta Lake, and major public cloud infrastructure and services. the product. You will use this expertise to become a trusted advisor to C&SI partners.

The impact you will have:

• Provide partners with the level of enablement they need to assist their clients in evaluating and adopting Databricks, including hands-on Apache Spark™ programming and integration with the wider cloud ecosystem.

• Engage with the partner technical community by leading workshops, seminars, and meet-ups.

• You will be a big data analytics expert in architecture and design, and will share this knowledge with our partner network.

• Show expertise by producing creative technical solutions and blog posts.

What we look for:

• 8 years of customer-facing experience working with external clients or partners across a variety of industry markets

• Core strength in either data engineering or data science

• Experience demonstrating technical concepts, including presenting and whiteboarding

• Experience developing architectures within a public cloud (AWS, Azure, or GCP)

• Hands-on experience in SQL, Python, Scala, or Java

• Expertise in at least one of the following:

• Data Engineering technologies (Ex, Apache Spark™, Hadoop, Kafka)

• Data Warehousing (Ex, SQL, OLTP/OLAP/DSS)

• Data Science and Machine Learning technologies (Ex, pandas, scikit-learn, HPO)

We're seeking a Head of Machine Learning to own and scale the ML function at Prodigal. This is the senior-most ML role in the company — partnering directly with the CTO as a peer technical leader, defining the multi-year ML vision, building and leading a world-class team, and translating cutting-edge research into production systems that move the needle for over 100 customers and millions of consumers. You'll set the bar for technical excellence, hire and develop the next generation of ML leaders, and shape what consumer finance intelligence looks like over the next decade. If you've operated at this level before — or are ready to step into it at a company where ML is the engine of the product — we want to talk.

Responsibilities:

• Own the ML function end-to-end — vision, roadmap, team, delivery, and measurable business impact across classical ML, NLP, GenAI, and Agentic AI
• Partner with the CTO as the senior ML leader to set company-wide technical direction and shape the long-term roadmap for Prodigal's Intelligence Engine
• Build and lead a high-performing ML organization — hire, mentor, and develop ML engineers, applied scientists, and tech leads across multiple pods
• Translate research into product — drive the application of LLMs, Agentic AI, the modern NLP stack, recommendation systems, and classical ML into production systems serving 100+ customers
• Set technical standards for model development, deployment, monitoring, evaluation, and experimentation that scale across the org
• Architect data and ML infrastructure in partnership with the platform and engineering teams to support large-scale training and reliable, low-latency inference
• Communicate strategy and outcomes clearly to the founders, board, customers, and broader engineering organization
• Represent Prodigal externally — at conferences, in research collaborations, and as a senior voice in the consumer finance AI community
• Stay at the frontier of ML and GenAI research, identifying where new techniques unlock product and business advantage
• Collaborate cross-functionally with product, engineering, GTM, and customer success leaders to ensure ML investment ties directly to revenue, retention, and customer outcomes

Requirements:

• 10+ years of experience in machine learning and software development at a tech or product company, with 4+ years leading ML teams (managing engineers and/or tech leads)
• Proven track record of shipping ML products in production at scale with measurable business impact
• Deep technical fluency across classical ML, deep learning, and the modern NLP/LLM stack — you can still go deep in design reviews, not just review at the surface
• Strong coding skills in Python. Hands-on with PyTorch, scikit-learn, Pandas, and Spark
• Demonstrated success hiring, mentoring, and growing ML talent — including team leads and senior engineers
• Strong executive communication and the ability to set technical direction with founders, peers, and the board
• A bias for outcomes — you connect model work to revenue, engagement, and operational metrics

Preferred Qualifications:

• Experience as a founding ML hire, ML lead, or applied science leader at a high-growth AI/ML product company
• Background in voice AI, conversational AI, speech, NLP on unstructured text, or fintech/lending domains
• Experience with cloud services (AWS), Databricks, and building scalable, distributed systems
• Track record of running A/B testing and experimentation programs at scale
• Patents, publications, or open-source contributions; strong institutional pedigree (IIT, BITS, NIT, IISc)
• Experience scaling an ML org through a hypergrowth phase

Job Benefits

• GenAI at the core – Lead the ML function at a company where AI isn't a feature, it's the product. Shape groundbreaking work that redefines consumer finance intelligence.
• World-class team - You'll get the chance to learn from (and teach) some of the brightest and most skilled people you'll ever meet. Our team members have been part of BCG, Deloitte, EY, Blackstone, Meta, Amazon and are IIT, IIM, BITS alumnus.
• Continuous education - As relentless seekers of knowledge, we sponsor and support any training materials, books, courses, and exam fees for upskilling yourself in areas related to your role - but that's not where it ends. If you make a good case for some extra learning, we're delighted to foot the tab.
• Food at the office – Meals are on us. We won't let your stomach grumble while you hustle.
• Health insurance - Health always comes first. The health of your family is as important to us as it is to you. We offer insurance coverage for both you and your family.
• Flexible schedule - We're not a "clock in, clock out" company. Morning person? Start work while the world's still sleeping. Night owl? Ramp things up while others are winding down. You're fully trusted to create the right conditions for your own personal peak productivity. We only ask that you be available to your teammates for seamless collaboration.
• Generous leave policy - Take all the time you need to recharge your batteries - no cap on vacation here. Because a rested, relaxed, and refreshed mind is a happy and effective mind.
• Recreation & team activities – We regularly indulge in friendly football, table tennis, cooking sessions, and challenge each other to do those extra push-ups or chug that 4th drink (of course, that's Nimbu Pani).

Meet Sangram (Cofounder and CTO) and hear his vision for Prodigal: https://www.youtube.com/embed/FTWlN9fwiIA?feature=shared

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology, and trading expertise has shaped our collaborative mindset, which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors.

Your future role within QRT

We are seeking a pragmatic and commercially-oriented Risk Technology specific Site Reliability Engineer (SRE) to join our team. The ideal candidate will have a strong background in technology. Primary he/she will be involved in prototyping, building scalable solutions with automated tests and CI/CD pipelines for the business with the goal of delivering a stable service. He/She must have a keen interest in automating processes and workflows. In addition, he/she will have to liaise with internal or external teams for effective management of the QRT Risk Technology stack. 

What you will do: 

• Develop and implement robust and reliable automation and/or analytical tools to help capture and investigate data quality problems using advanced Python 
• Building and enhancing user interfaces using tools like Plotly Dash and/or Streamlit. 
• Monitor system performance and troubleshoot issues to ensure optimal operation. 
• Collaborate with other support and development teams, SRE or Infrastructure teams to improve system reliability and performance. 
• Ensure security and compliance standards are met. 
• Support Risk and PnL application infrastructure, including but not limited to: 
• Risk and PnL application problems 
• AWS EKS infrastructure issues 
• Monitoring and react to system issues 

Requirements 

• Good understanding of system architecture and cloud technologies. 
• Proficiency in programming languages, Python being the primary. 
• Experience with monitoring and logging tools. 
• Excellent problem-solving skills and attention to detail. 
• Ability to work in a fast-paced, dynamic environment. 
• Commercially oriented mindset with a focus on business outcomes. 
• Prior experience in the finance industry is preferred but not required. 

Your present skillset 

• 4-7 years of software development experience. 
• Advanced proficiency in Python with strong object-oriented programming skills. 
• Advanced proficiency with the use of pandas DataFrame. 
• UI development experience with python frameworks such as Plotly Dash and/or streamlit. 
• Experience with self-hosted and cloud platforms (AWS preferred). 
• Familiarity with Rest APIs, Containers, Kubernetes, and SQL Server. 
• Strong problem-solving skills with a proactive and efficient approach. 
• Good clear communication skill with the ability to navigate and express complex ideas. 
• Ability to manage time effectively. 
• Familiarity with risk and PnL concepts (good-to-have). 
• Familiar with time series data visualization and technology such as InfluxDB, Prometheus (good-to-have). 

QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work-life balance.

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology and trading expertise has shaped QRT’s collaborative mindset which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors.

We are looking for an exceptional Data Scientist to join the Data Search & Analytics team. In this role, you will work directly with the Research and Trading desks to improve their productivity and efficiency.  

Your future role within QRT  

This team is integral to the firm’s success. As such, your responsibilities will include:  

• Sourcing, designing and onboarding new datasets according to trading desks’ priorities  
• Becoming an expert on QRT’s internal tools to help the relevant desks efficiently, using data in production of trading systems  
• Proactively solving data related problems in order to minimise time to production  
• Managing the full lifecycle of data sourcing projects, which includes data acquisition/exploration/engineering, prototyping and production  

Your present skillset  

• 3+ years of experience as a Data Scientist (or similar position); experience in a buy-side quantitative finance role is advantageous
• Postgraduate degree in a quantitative discipline such as Mathematics, Physics or Engineering.
• Advanced programming experience in Python, including proficiency with data handling libraries such as Pandas and NumPy
• Demonstratable interest in financial markets and the application of data in its analysis and understanding
• Experience working with both traditional and alternative financial datasets
• Excellent communication skills, with the ability to effectively collaborate with all stakeholders, including researchers, traders, engineers, management, and external vendors
• Ability to work in a high-performance, high-velocity environment

QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work-life balance.

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology, and trading expertise has shaped QRT’s collaborative mindset which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors. 

We are looking for an exceptional Data Scientist to join the Data Search & Analytics team. In this role, you will work between the Research and Trading desks, and the Engineering team to ensure the successful leveraging of data at the firm.

Your future role within QRT

This team is integral to the firm’s success. As such, your responsibilities will include:

• Collaborating with Quantitative Researchers and Traders to design datasets that drive systematic strategies and to inform discretionary trading decisions
• Prototyping and designing code to extract, clean, and aggregate data from a wide range of raw sources and formats
• Working with Engineers to automate and optimise your code, ensuring robust data extraction processes
• Managing the end-to-end process of onboarding new datasets
• Proactively solving data related problems to minimise time to production
• Innovating and experimenting with novel data extraction methods to enhance the firm’s data onboarding toolkit

Your present skillset

• 3+ years of experience as a Data Scientist (or similar position); experience in a buy-side quantitative finance role is advantageous
• Postgraduate degree in a quantitative discipline such as Mathematics, Physics or Engineering.
• Advanced programming experience in Python, including proficiency with data handling libraries such as Pandas and NumPy
• Demonstratable interest in financial markets and the application of data in its analysis and understanding
• Experience working with both traditional and alternative financial datasets
• Excellent communication skills, with the ability to effectively collaborate with all stakeholders, including researchers, traders, engineers, management, and external vendors
• Ability to work in a high-performance, high-velocity environment

QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work-life balance.

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology, and trading expertise has shaped QRT’s collaborative mindset which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors.

We are looking for an exceptional Data Scientist to join the Data Search & Analytics team. In this role, you will work between the Research and Trading desks, and the Engineering team to ensure the successful leveraging of data at the firm.

Your future role within QRT

This team is integral to the firm’s success. As such, your responsibilities will include:

• Collaborating with Quantitative Researchers and Traders to design datasets that drive systematic strategies and to inform discretionary trading decisions
• Prototyping and designing code to extract, clean, and aggregate data from a wide range of raw sources and formats
• Working with Engineers to automate and optimise your code, ensuring robust data extraction processes
• Managing the end-to-end process of onboarding new datasets
• Proactively solving data related problems to minimise time to production
• Innovating and experimenting with novel data extraction methods to enhance the firm’s data onboarding toolkit

Your present skillset

• 3+ years of experience as a Data Scientist (or similar position); experience in a buy-side quantitative finance role is advantageous
• Postgraduate degree in a quantitative discipline such as Mathematics, Physics or Engineering.
• Advanced programming experience in Python, including proficiency with data handling libraries such as Pandas and NumPy
• Demonstratable interest in financial markets and the application of data in its analysis and understanding
• Experience working with both traditional and alternative financial datasets
• Excellent communication skills, with the ability to effectively collaborate with all stakeholders, including researchers, traders, engineers, management, and external vendors
• Ability to work in a high-performance, high-velocity environment

QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work-life balance.

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology, and trading expertise has shaped QRT’s collaborative mindset which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors.

We are looking for an exceptional Data Scientist. In this role, you will work between the Research and Trading desks, and the Engineering team to ensure the successful leveraging of data at the firm.

Your future role within QRT

This team is integral to the firm’s success. As such, your responsibilities will include:

• Collaborating with the trading desk to design and implement their data pipelines
• Collaborating with data engineers to onboard relevant datasets for the desk within QRT
• Implementing data transformations to produce high added-value datasets used for investment decisions
• Prototyping and designing code to extract, clean, and aggregate data from a wide range of raw sources and formats
• Managing the end-to-end process of onboarding new datasets
• Proactively solving data related problems to minimise time to production
• Innovating and experimenting with novel data extraction methods to enhance the firm’s data onboarding toolkit

Your present skillset

• 3+ years of experience as a Data Scientist (or similar position); experience in a buy-side quantitative finance role is advantageous
• Postgraduate degree in a quantitative discipline such as Mathematics, Physics or Engineering.
• Advanced programming experience in Python, including proficiency with data handling libraries such as Pandas and NumPy
• Demonstrable interest in financial markets and the application of data in its analysis and understanding
• Experience working with both traditional and alternative financial datasets
• Excellent communication skills, with the ability to effectively collaborate with all stakeholders, including researchers, traders, engineers, management, and external vendors
• Ability to work in a high-performance, high-velocity environment

QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work-life balance.

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology, and trading expertise has shaped QRT’s collaborative mindset which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors.

We are looking for an exceptional Data Scientist to join the Data Search & Analytics team. In this role, you will work between the Research and Trading desks, and the Engineering team to ensure the successful leveraging of data at the firm.

Your future role within QRT

This team is integral to the firm’s success. As such, your responsibilities will include:

• Collaborating with the trading desk to design and implement their data pipelines
• Collaborating with data engineers to onboard relevant datasets for the desk within QRT
• Implementing data transformations to produce high added-value datasets used for investment decisions
• Prototyping and designing code to extract, clean, and aggregate data from a wide range of raw sources and formats
• Managing the end-to-end process of onboarding new datasets
• Proactively solving data related problems to minimise time to production
• Innovating and experimenting with novel data extraction methods to enhance the firm’s data onboarding toolkit

Your present skillset

• 3+ years of experience as a Data Scientist (or similar position); experience in a buy-side quantitative finance role is advantageous
• Postgraduate degree in a quantitative discipline such as Mathematics, Physics or Engineering.
• Advanced programming experience in Python, including proficiency with data handling libraries such as Pandas and NumPy
• Demonstrable interest in financial markets and the application of data in its analysis and understanding
• Experience working with both traditional and alternative financial datasets
• Excellent communication skills, with the ability to effectively collaborate with all stakeholders, including researchers, traders, engineers, management, and external vendors
• Ability to work in a high-performance, high-velocity environment

QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work-life balance.

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology, and trading expertise has shaped QRT’s collaborative mindset which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors.

We are looking for an exceptional Data Scientist with a strong fixed income background. In this role, you will work between the Research and Trading desks, and the Engineering team to ensure the successful leveraging of data across the firm's fixed income strategies and beyond.

Your future role within QRT

This team is integral to the firm's success. As such, your responsibilities will include:

• Collaborating with Quantitative Researchers and Traders, particularly within fixed income, to design datasets that drive systematic strategies and inform discretionary trading decisions
• Prototyping and designing code to extract, clean, and aggregate data from a wide range of raw sources and formats, including fixed income market data, reference data, and pricing feeds
• Working with Engineers to automate and optimise your code, ensuring robust data extraction processes
• Managing the end-to-end process of onboarding new datasets, with a focus on fixed income instruments such as bonds, rates, credit, and derivatives
• Proactively solving data related problems to minimise time to production
• Innovating and experimenting with novel data extraction methods to enhance the firm's data onboarding toolkit

Your present skillset

• 2+ years of experience as a Data Scientist (or similar position) working with fixed income data; experience in a buy-side quantitative finance role is strongly preferred
• Solid understanding of fixed income markets and instruments including bonds, interest rates, credit, yield curves, or related derivatives
• Postgraduate degree in a quantitative discipline such as Mathematics, Physics or Engineering
• Advanced programming experience in Python, including proficiency with data handling libraries such as Pandas and NumPy
• Proven experience building and managing data pipelines end-to-end; from sourcing and extraction through to cleaning, processing, and delivery
• Excellent communication skills, with the ability to effectively collaborate with all stakeholders, including researchers, traders, engineers, management, and external vendors
• Ability to work in a high-performance, high-velocity environment

QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work-life balance.

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology, and trading expertise has shaped QRT’s collaborative mindset which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors.

We are looking for an exceptional Data Scientist with strong experience in index data. In this role, you will work between the Research and Trading desks, and the Engineering team to ensure the successful leveraging of index data across the firm's systematic strategies.

Your future role within QRT

This team is integral to the firm's success. As such, your responsibilities will include:

• Collaborating with Quantitative Researchers and Traders to design and maintain index and ETF datasets that drive systematic strategies and inform discretionary trading decisions

• Prototyping and designing code to extract, clean, and aggregate data from a wide range of raw sources and formats, including index constituent data, benchmark compositions, weightings, rebalancing schedules, ETF holdings and flows, and associated reference data

• Working with Engineers to automate and optimise your code, ensuring robust data extraction processes

• Managing the end-to-end process of onboarding new index and ETF datasets, including equity indices, fixed income indices, ETFs, custom baskets, and related benchmark products

• Proactively solving data related problems to minimise time to production

• Innovating and experimenting with novel data extraction methods to enhance the firm's data onboarding toolkit

Your present skillset

• 2+ years of experience as a Data Scientist (or similar position) working with index or ETF data, benchmark data, or index-linked financial products; experience in a buy-side quantitative finance role is strongly preferred

• Solid understanding of index construction, constituent methodology, rebalancing mechanics, ETF structure and flows, and the role of benchmarks in systematic and portfolio-level strategies

• Postgraduate degree in a quantitative discipline such as Mathematics, Physics or Engineering

• Advanced programming experience in Python, including proficiency with data handling libraries such as Pandas and NumPy

• Proven experience building and managing data pipelines end-to-end; from sourcing and extraction through to cleaning, processing, and delivery

• Excellent communication skills, with the ability to effectively collaborate with all stakeholders, including researchers, traders, engineers, management, and external vendors

• Ability to work in a high-performance, high-velocity environment

QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work-life balance.

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology, and trading expertise has shaped our collaborative mindset, which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors.

Your future role within QRT

• Design, develop, and maintain software solutions with clean, modern architecture
• Partner with front office to drive technical solutions around new trading initiatives
• Communicate with quants and traders regularly to deliver bespoke solutions
• Bring in new ideas and insights using data driven pipelines and analytics

Your present skillset

• 2-8 years of software development experience
• Solid experience in Python and core libraries such as Numpy, Pandas is a must
• Proven understanding of modern software architecture
• Strong computer science fundamentals
• Experience with Linux environments, Cloud Platforms (AWS preferred) and Containerized applications (Docker preferred)
• Experience with other programming languages such as Java, C++, C# is a plus, but not necessary
• Agility to work in a fast-paced environment with varying demands
• Strong team-player
• Strong communication skills essential
• Previous experience in finance is an advantage, but not a must

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology, and trading expertise has shaped our collaborative mindset, which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors.

Your future role within QRT

We are seeking a pragmatic and commercially oriented Production Support Engineer with a strong Site Reliability Engineering (SRE) orientation to join our Risk Technology team. This role is primarily focused on ensuring stability, availability, and performance of critical trading, risk, and PnL systems in a fast-paced production environment.

The ideal candidate will have a solid background in supporting production systems, combined with an SRE mindset focused on automation, reliability, and continuous improvement. You will be responsible for monitoring live systems, managing incidents, troubleshooting complex issues, and driving initiatives that enhance system resilience and reduce operational overhead.

In addition to hands-on production support, you will contribute to improving system reliability through automation, proactive monitoring, and collaboration with development and infrastructure teams. You will play a key role in bridging support and engineering by identifying recurring issues, performing root cause analysis, and implementing long-term fixes aligned with SRE principles.

You will work closely with internal and external teams to ensure effective management, scalability, and reliability of the QRT Risk Technology stack while maintaining a strong focus on business continuity and operational excellence.

What will you do

• Provide end-to-end production support alongside the global team for post trade flows, ensuring high availability and performance
• Monitor production systems, investigate alerts, and respond to incidents in a timely manner
• Automate repetitive operational tasks to reduce manual intervention
• Perform root cause analysis (RCA) and implement preventive measures
• Troubleshoot issues across applications, infrastructure, and data layers
• Collaborate with development teams to improve system stability and release quality
• Ensure data quality and integrity across risk and PnL workflows
• Participate in on-call rotations and handle production incidents
• Participate in operational processes improvements, incidents management, and resolutions

Requirements

• Strong understanding of production support and incident management processes
• Experience with operating and configuration of monitoring, alerting, and logging tools
• Good understanding of system architecture and cloud technologies (AWS preferred)
• Proficiency in Python for troubleshooting and automation
• Strong analytical, problem-solving skills and ability to function independently.
• Ability to work in a fast-paced, business-critical environment
• Effective communication skills across technical and business stakeholders
• Commercially oriented mindset with focus on business impact
• Prior experience in finance is preferred but not required

Your present skillset

• 4+ years of experience in production support, or software engineering
• Strong Python skills and (good to have ) understanding of object-oriented programming
• Experience using pandas DataFrame for data analysis
• Experience with cloud platforms (AWS preferred)
• Experience with monitoring tools such as Prometheus, Grafana, or similar
• Strong problem-solving skills with a proactive approach
• Clear communication skills and ability to articulate complex ideas
• Good time management and prioritization skills
• Familiarity with risk and PnL concepts (good-to-have)
• Exposure to time-series data and tools like InfluxDB (good-to-have)
• Familiarity with REST APIs, Containers, Kubernetes, and SQL Server (good-to-have)
• UI development experience with Plotly Dash and/or Streamlit (good-to-have)

QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work-life balance.

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a group driven by technology and data, implementing a scientific approach to investing. Combining data, research, technology and trading expertise has shaped our collaborative mindset, which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors.

You will work on systems and tooling at the intersection of trading infrastructure and market microstructure. The role focuses on building data pipelines, analytics and libraries that support execution analysis and improve trading performance. You will operate close to the trading stack, working with high resolution datasets and contributing to both research and production systems.

Your future role within QRT

• Analyse trading behaviour across venues to identify execution inefficiencies and improve latency and fill performance
• Develop Python libraries, data pipelines and tooling to process exchange protocols, order flow and large scale trading data
• Build analytical frameworks, metrics and monitoring tools to evaluate execution quality and system performance
• Contribute to production systems supporting live trading, including data validation, pipeline reliability and real time monitoring
• Collaborate with stakeholders to translate analytical insights into measurable improvements in execution performance
• Work with research and infrastructure teams to support ongoing development of execution analysis and market behaviour understanding

Your present skillset

• Degree in Computer Science, Engineering, Mathematics or a related quantitative field
• 7+ years of professional experience in Python development, building libraries, data platforms or analytical tooling
• Strong experience working with large scale and time series datasets using tools such as NumPy and Pandas
• Solid SQL knowledge and experience working with analytical databases
• Strong problem solving skills with the ability to investigate complex data and system behaviours
• Ability to translate analytical or trading related problems into robust engineering solutions
• Experience working in Linux environments and applying software engineering best practices such as testing, version control and continuous integration
• Strong communication skills with the ability to collaborate across technical and non technical stakeholders
• Familiarity with financial markets, electronic trading or execution analysis
• Exposure to network protocols, packet analysis or performance optimisation techniques is beneficial
• Experience with lower level programming languages such as C, C++ or Rust is advantageous

QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work life balance.