The Team

The Biohub in New York is an independent nonprofit research institute that brings together three powerhouse universities - Columbia University, The Rockefeller University, and Yale University - into a single collaborative technology and discovery engine. Biohub itself supports some of the brightest, boldest engineers, data scientists, and biomedical researchers to investigate the fundamental mechanisms underlying disease and develop new technologies that will lead to actionable diagnostics and effective therapies. We are guided by our values of scholarly excellence; disruptive innovation; hands-on engineering/hacking/building; partnership and collaboration; open communication and respect; inclusiveness; and opportunity for all.

Our Vision                                                                     

• We pursue large scientific challenges that cannot be pursued in conventional environments
• We enable individual investigators to pursue their riskiest and most innovative ideas
• The technologies developed at Biohub facilitate research by scientists and clinicians at our home institutions and beyond

Diversity of thought, ideas, and perspectives are at the heart of Biohub and enable disruptive innovation and scholarly excellence. We are committed to cultivating an organization where all colleagues feel inspired and know their work makes an important contribution.

The Opportunity

The Biohub is seeking an accomplished Scientist  I to lead innovative investigations into spatial architecture of molecular systems and its role in controlling cell state dynamics. In our newly established Synthetic Spatial Omics Laboratory, we advance next-generation spatial multi-omics platforms for understanding how cells process combinatorial signals, designing synthetic biology toolkits, and capturing cellular temporal histories with spatial resolution.

The successful candidate will lead independent research projects integrating spatial multi-omics, synthetic biology, and systems-level analysis to investigate molecular neighborhood reorganization in response to perturbations, engineer programmable cellular functions, and identify the molecular drivers of cell state transitions. This position offers the opportunity to drive both technology development and fundamental discovery in a highly collaborative, interdisciplinary research environment. 

What You'll Do

• Lead the development and application of imaging-based multi-omics technologies (e.g., seqFISH and related approaches) to map subcellular molecular organization and cell state transitions.
• Design and perform perturbation experiments to decode how molecular neighborhoods respond to combinatorial signals.
• Engineer synthetic biology systems and profile their behavior using spatial multi-omics to understand and program cellular functions.
• Lead analysis of high-dimensional imaging and sequencing datasets using computational and machine learning approaches to identify organizational and regulatory principles. 
• Collaborate closely with researchers across genomics, synthetic biology, machine learning, and related disciplines.
• Contribute to and lead preprints, publications, presentations, and open science practices.

What You'll Bring

Essential - 

• PhD in Systems Biology, Molecular Biology, Immunology, Bioengineering or a closely related discipline.
• 3-4+ years of postdoctoral or equivalent research experience, with a strong record of scientific productivity.
• Demonstrated expertise in imaging-based omics and/or sequencing-based single-cell technologies.
• Expertise in molecular biology techniques, including cell culture, nucleic acid workflows, and cellular functional assays.
• Strong computational skills in Python, R, or comparable languages, with experience analyzing large-scale imaging and sequencing datasets.
• Proven ability to independently conceptualize research directions, design rigorous experiments, troubleshoot complex workflows, and lead projects from conception to publication.
• Collaborative mindset and strong scientific communication skills.

Nice to have - 

• Established track record in developing novel experimental or computational technologies.
• Prior work with imaging-based transcriptomics or genomics technologies.
• Prior work with immune cell types or immune cell engineering.
• Expertise in synthetic biology, CRISPR technologies, or engineered cell systems.
• Experience with temporal genomics, molecular recording, or lineage-tracing approaches.
• Background in regulatory network analysis, signaling dynamics, or models of cellular computation.

Compensation

The New York City, NY base pay range for a new hire in this role is $120,000.00 - $150,000.00. New hires are typically hired into the lower portion of the range, enabling employee growth in the range over time. Actual placement in range is based on job-related skills and experience, as evaluated throughout the interview process. 

Benefits for the Whole You 

We’re thankful to have an incredible team behind our work. To honor their commitment, we offer a wide range of benefits to support the people who make all we do possible. 

• Provides a generous employer match on employee 401(k) contributions to support planning for the future.
• Paid time off to volunteer at an organization of your choice. 
• Funding for select family-forming benefits. 
• Relocation support for employees who need assistance moving

If you’re interested in a role but your previous experience doesn’t perfectly align with each qualification in the job description, we still encourage you to apply as you may be the perfect fit for this or another role.

#LI-Hybrid #LI-Onsite

About the Team

The Discovery team is organized around the north star of building an AI scientist — a system capable of solving the long-horizon reasoning challenges and core capabilities needed to push the scientific frontier. The team trains large-scale models, runs complex multi-week experiments, and builds novel products at the intersection of AI and science.

About the Role

As a Technical Program Manager on the Discovery team, you will own the systems and programs that determine how fast our research moves: compute planning, scientific RL environment health, and the vendor pipelines that supply them, with scope to incubate new programs in domains like bio R&D. Strong candidates should have an ML engineering or research background and have grown into program leadership. You'll need real technical depth: the ability to debug data pipelines, read RL transcripts to spot issues, and make allocation and quality decisions in real time when experimental or production runs hit problems. You'll need organizational effectiveness in equal measure: the ability to navigate a fast-growing organization, quickly identify the critical people and teams across research, infrastructure, product, and data operations, and coordinate across them without losing velocity.

Join us in our mission to build AI systems that push the frontiers of science and benefit humanity.

Responsibilities

• Manage Discovery's compute planning across supervised learning (SL), reinforcement learning (RL), and sandboxing workloads, including forecasting, allocation, prioritization, and efficiency improvements. Partner with central compute planning to ensure Discovery's needs are represented and met.
• Monitor the health of scientific RL environments (quality, reward integrity, failure rates) and drive issues to resolution.
• Expedite the external vendor pipeline for RL environments, including quality review, reward design, and production integration.
• Work with research teams across life sciences, STEM, and other scientific domains to translate research goals into roadmaps that advance AI scientist capabilities.
• Establish processes and frameworks that bring structure to an unstructured research setting without slowing researchers down.
• Collaborate with research leads, infrastructure engineers, and data operations to identify blockers, prioritize competing needs, and make technical trade-off decisions.

You May Be a Good Fit If You:

• Have a background in ML engineering, ML research, or STEM R&D before transitioning to technical program management
• Have deep, hands-on experience with ML training pipelines, RLHF systems, and large-scale data infrastructure in production.
• Have a track record of building execution plans and inventing high-leverage processes that reduce operational overhead and let researchers focus on research.
• Are a fast learner who builds deep contextual understanding in unfamiliar technical domains and can contribute meaningfully to discussions with researchers.
• Are resourceful, high-agency, and able to navigate ambiguity and shifting priorities to drive progress in a fast-moving research setting
• Have excellent stakeholder management and communication skills, with the ability to influence senior technical staff through clarity, competence, and consistent delivery.
• Are excited about the potential for AI to accelerate scientific discovery

Code and Theory is seeking a senior cloud engineer who wants to own the full technical lifecycle of enterprise client deployments — from assessing a client's existing infrastructure and designing the integration architecture, to provisioning the environment and keeping it running in production. This is a hands-on role that also requires real client engagement: you will sit in technical working sessions, work directly with client DevOps teams, and be the person who translates security and compliance requirements into concrete infrastructure decisions.

You will be working on a platform that connects our AI product to enterprise marketing environments across different cloud providers. Each client brings its own infrastructure, data stack, and security perimeter. Your job is to figure out how to connect to it, build the integration reliably, and hand it off in a state that can be operated and maintained over time.

WHAT YOU'LL DO

• Assess each client's cloud infrastructure, data stack, and security perimeter before any build starts — translate findings into a concrete integration plan
• Design and provision client environments using Terraform — networking, IAM, container orchestration, managed storage, and secrets management across GCP, AWS, or Azure depending on the client
• Deploy and operate LLM inference pods in client cloud environments — managing API integration, rate limits, latency, and failure handling without needing a data scientist in the room
• Build and maintain integration layers that connect client data sources to the AI layer — you own the plumbing that makes inference useful; the data science team owns what runs on top of it
• Deploy and maintain containerized workloads via Helm — orchestration, ETL workers, and AI inference pods running inside the client's cloud perimeter
• Own data pipeline deployments end to end — scheduling, pagination, retry logic, and rate-limit management against client API gateways
• Manage distributed ETL jobs at scale — JSON flattening, schema enforcement, and structured output delivery
• Enforce data residency requirements — raw data stays inside the client's environment, only structured output leaves to our shared infrastructure
• Serve as the primary technical contact for each client's DevOps and infrastructure teams throughout the engagement
• Lead technical working sessions with client teams — validate configurations, confirm IAM and credential models, review cluster specs before deployment
• Triage and resolve pipeline and infrastructure failures across multiple client environments simultaneously
• Implement container security standards — non-root execution, read-only filesystems, startup integrity hashes, tamper protection
• Mentor junior and mid-level engineers and contribute implementation patterns to the shared playbook after every client engagement

WHAT YOU'LL NEED

• 6+ years of cloud engineering experience — production experience on at least two of GCP, AWS, or Azure, and willing to operate across all three depending on client environment
• Deep Terraform experience — you have provisioned multi-environment, multi-tenant production infrastructure from scratch, not just applied existing configurations
• Comfortable with Kubernetes and Helm in production — deploying, debugging, scaling, and securing containerized workloads, not just running them
• Have run data pipelines in production — you know what breaks, how to recover, and how to build retry and backoff logic that actually holds up
• Hands-on experience with distributed processing at scale — not just theoretical knowledge
• Enterprise API integration experience — OAuth 2.0, API key management, rate limiting, API gateways — end to end
• Solid security fundamentals — container hardening, credential management, and data residency enforcement in environments where it actually matters
• Have worked directly with client or customer engineering teams — comfortable leading technical conversations with enterprise DevOps and infrastructure teams, not just supporting them
• You write things down — runbooks, integration notes, playbook contributions. The next engineer should be able to operate what you built
• Hands-on experience integrating LLM APIs (Anthropic, OpenAI, or equivalent) into production pipelines — not as an end user, but wiring inference into systems that run at scale across real client environments

NICE TO HAVE

• Experience with:
  • LLM inference infrastructure
  • Marketing technology stacks (DAM, CDP, CRM)
  • Multi-tenant client environments
• A cloud certification (AWS Solutions Architect, GCP Professional Cloud Architect, or Azure equivalent)
• Agency, consultancy, or product company background serving multiple enterprise clients simultaneously

ABOUT US

Born in 2001, Code and Theory is a digital-first creative agency that sits at the center of creativity and technology. We pride ourselves on not only solving consumer and business problems, but also helping to establish new capabilities for our clients. With a global client roster of Fortune 100s and start-ups alike, we crave the hardest problems to solve. We have teams distributed across North America, South America, Europe, and Asia. The Code and Theory global network of agencies is growing and includes Kettle, Instrument, Left Field Labs, Create Group, Current, and TrueLogic.

Striving never to be pigeonholed, we work across every major category: from tech to CPG, financial services to travel & hospitality, government and education to media and publishing. We value the collaboration with our client partners, including but not limited to Adidas, Amazon, Con Edison, Diageo, EY, J.P. Morgan Chase, Lenovo, Marriott, Mars, Microsoft, Thomson Reuters, and TikTok.

The Code and Theory network is comprised of nearly 2,000 people with 50% engineers and 50% creative talent. We’re always on the lookout for smart, driven, and forward-thinking people to join our team.

The target range of base compensation for this role is $110,000 - $150,000. Actual compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, and location.

Hi, we're Oscar. We're hiring an Applied AI Engineer to join our AI team.

Oscar is the first health insurance company built around a full stack technology platform and a focus on serving our members. We started Oscar in 2012 to create the kind of health insurance company we would want for ourselves—one that behaves like a doctor in the family.

About the role

Join Oscar’s AI team to build and launch transformative AI-powered products that redefine the healthcare experience for millions. As an Applied AI Engineer, you’ll take end-to-end ownership of high-impact projects—conceiving, prototyping, and deploying generative AI solutions that solve real-world problems across the healthcare ecosystem.You’ll work alongside a world-class team of engineers and data scientists, rapidly iterating on ideas and shipping production-ready applications that drive measurable business and user outcomes. You’ll have the autonomy to identify opportunities, experiment with the latest AI models and techniques, and deliver solutions that streamline operations, improve patient experiences, and move the needle for Oscar and our members.

You will report to the Sr. Director, AI.

Work Location: This position is based in our New York City office, requiring a hybrid work schedule with 3 days of in-office work per week. Thursdays are a required in-office day for team meetings and events, while your other two office days are flexible to suit your schedule. #LI-Hybrid

Pay Transparency: The base pay for this role is: $163,944 - $236,911 per year. You are also eligible for employee benefits, participation in Oscar’s unlimited vacation program, company equity grants, and annual performance bonuses.

Responsibilities

• Lead the design, development, and deployment of AI-driven products and features, from zero-to-one prototypes to robust production systems.
• Collaborate cross-functionally with product, design, and business teams to deeply understand user needs and deliver solutions that move key metrics.
• Rapidly experiment with new models, techniques, and tools (including LLMs, RAG, and agentic workflows), iterating based on data and user feedback.
• Build and scale full-stack applications and intelligent agents that automate complex healthcare workflows and deliver seamless user experiences.
• Stay on the cutting edge of the AI ecosystem, evaluating and integrating emerging technologies to keep Oscar at the forefront of healthcare innovation.
• Contribute to Oscar’s AI strategy and technical vision, helping shape the future of healthcare through technology.
• Other duties as assigned

Qualifications

• 7+ years of professional experience in software engineering, data science, or a related field, with a track record of impactful accomplishments
• Strong Python programming skills
• Experience solving complex, ambiguous problems 
• Experience designing and analyzing experiments or evaluations
• Experience or an interest in prototyping full-stack applications

Bonus Points

• Experience with the healthcare industry. Familiarity with claims or clinical data.
• Degree in math, statistics, data science, or engineering.
• Our ideal candidate is a data scientist with an engineering background, an engineer with a data science background, or a founder-type who combines product thinking, rapid prototyping, and hands-on coding to deliver real-world impact.
• Experience with NLP, neural networks, transformers, and modern AI frameworks.
• Knowledge of containerization and deployment tools (e.g., Docker, Kubernetes) or full-stack development (e.g., React, Golang).
• Experimental mindset: comfort with testing hypotheses and iterating on failures.

Marketing Scientist, Creator Monetization

About the role

Beast Industries is building the operating system for creator monetization — the connective tissue between the world's biggest creators and the brands that want to reach their audiences. Vyro is our brand-creator marketplace, and we believe there's a real opportunity to do something here that no one else in the space is set up to do.

Where's the real incrementality — and how much is it worth?

That question is the entire job.

We're hiring a Marketing Scientist to design and run the experiments that will help us answer it. You'll build a portfolio of incrementality tests, lift studies, and measurement frameworks — starting scrappy, getting sharper with every campaign, and using what we learn to inform what we build, what we sell, and where we double down next.

This is an early role on a team still finding its shape. You'll work directly with leadership, sit close to the engineers and product people building Vyro, and have a direct line to the creative talent that powers Beast. The strategy isn't fully baked. The org isn't fully built. Some of the answers don't exist yet — that's part of the appeal. You'll be one of the people figuring them out.

What you'll do

• Help us run our first real campaigns and learn from them — fast. Design tests that get useful answers in weeks, not quarters, and tighten the methodology with each iteration.
• Build the measurement playbook as we go: holdouts, geo-experiments, matched markets, retargeting studies, creative variant testing — picking the right tool for the question in front of us, not the most elegant one in the textbook.
• Partner with our buy-side operators on retargeting strategy, turning campaign exposure data into audiences and quantifying what those audiences are actually worth.
• Translate findings into clear, brand-facing case studies and internal product decisions. Every campaign should teach us something, and that something should sharpen the next sales conversation or product bet.
• Work directly with creators, creative leads, sales, and engineering — sometimes all in the same week, often without clean handoffs. Push hypotheses about what makes sponsored content actually convert: pacing, placement, call-to-action, narrative integration, and the subtler factors no one has quantified yet.
• Help us figure out where creator data should and shouldn't flow. You'll be a key voice on what's measurable, what's defensible, and where our long-term advantage lives.

Who you are

You have 2–4 years of experience and have already shipped measurement work that influenced real decisions. You're past needing your hand held on study design, but early enough in your career that you're still hungry — fired up by a clean experimental result, irritated when someone hand-waves a number.

You can hold a tangle of moving parts in your head and explain it back simply. You see the levers and the knock-on effects where others see noise, and you can walk a brand, a creator, or a CFO through that picture without losing them. You're relentless about the unglamorous work — chasing the loose thread, building the tracker no one else wanted to build, keeping the study honest when it would be easier not to. And underneath it all, you genuinely like the craft: spreadsheets, queries, models. That's the part of the day you'd rather not stop doing.

You're allergic to false precision and process for its own sake. You'd rather ship a rough answer this week than a perfect one next quarter, and you know which questions are worth which level of rigor.

Required experience

• 2–4 years in a marketing science, ad measurement, experimentation, or analytics role — with hands-on responsibility for experiments that shipped and influenced decisions.

• Currently or recently at one of two kinds of places:

• A platform creator-monetization team: YouTube Creator Partnerships (formerly BrandConnect), the YouTube Partner Program, or YouTube Shopping; TikTok One (which now houses Creator Marketplace and Spark Ads) or TikTok Shop; Meta's Instagram Creator Marketplace or the Partnership Ads team (formerly Branded Content Ads).
• A measurement-focused performance shop: an incrementality, attribution, or media mix modeling platform (Measured and Northbeam are good examples) — somewhere you ran or supported real lift tests for paying brands.

• Working knowledge of incrementality testing, causal inference fundamentals, and the limits of last-click attribution. You don't need a PhD — you need to know which tool fits the question.
• Comfort with SQL and a scripting language (Python or R). You can pull your own data, build your own models, and aren't waiting on someone else's dashboard to do your job.

Bonus points

• Experience with cross-platform measurement — quantifying lift when the same campaign runs on YouTube and TikTok and Meta.
• You've shipped a real geo-experiment or matched-market test in the wild and have opinions about why most of them are designed badly.
• Familiarity with platform APIs across the major social ecosystems and a clear-eyed sense of what they do and don't expose.
• You've worked closely with creators or talent before and understand that the creative side of this work matters as much as the math.

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.

Scale Labs, Research Scientist — Frontier Risk Evaluations

As the leading data and evaluation partner for frontier AI companies, Scale plays an integral role in understanding the capabilities and safeguarding AI models and systems. Building on this expertise, Scale Labs has launched a new team focused on policy research, to bridge the gap between AI research and global policymakers to make informed, scientific decisions about AI risks and capabilities.

Our research tackles the hardest problems in agent robustness, AI control protocols, and AI risk evaluations to help governments, industry, and the public understand and mitigate AI risk while maximizing AI adoption. This team collaborates broadly across industry, the public sector, and academia and regularly publishes our findings. We are actively seeking talented researchers to join us in shaping this vision.

As a Research Scientist focused on Frontier Risk Evaluations, you will design and create evaluation measures, harnesses and datasets for measuring the risks posed by frontier AI systems. For example, you might do any or all of the following: 

• Design and build harnesses to test AI models and systems (including agents) for dangerous capabilities such as security vulnerability exploitation, CBRN uplift, and other high-risk activities;
• Work with government agencies or other labs to collectively scope and design evaluations to measure and mitigate risks posed by advanced AI systems;
• Publish evaluation methodologies and write technical reports for policymakers.

Ideally you’d have:

• Commitment to our mission of promoting safe, secure, and trustworthy AI deployments in the industry as frontier AI capabilities continue to advance.
• Practical experience conducting technical research collaboratively. You should be comfortable building and instrumenting ML pipelines, writing evaluation harnesses, and quickly turning new ideas from the research literature into working prototypes.
• A track record of published research in machine learning, particularly in generative AI.
• At least three years of experience addressing sophisticated ML problems, whether in a research setting or in product development.
• Strong written and verbal communication skills to operate in a cross-functional team.

Nice to have:

• Experience in crafting evaluations and benchmarks, or a background in data science roles related to LLM technologies.
• Experience with red-teaming or adversarial testing of AI systems.
• Familiarity with AI safety policy frameworks (e.g., NIST AI RMF, EU AI Act, Korea AI Basic Act).

Our research interviews are crafted to assess candidates' skills in practical ML prototyping and debugging, their grasp of research concepts, and their alignment with our organizational culture. We will not ask any LeetCode-style questions. If you’re excited about advancing AI safety and contributing to our mission, we encourage you to apply, even if your experience doesn’t perfectly align with every requirement.

Scale Labs, Research Scientist — Agent Robustness

As the leading data and evaluation partner for frontier AI companies, Scale plays an integral role in understanding the capabilities and safeguarding AI models and systems. Building on this expertise, Scale Labs has launched a new team focused on policy research, to bridge the gap between AI research and global policymakers to make informed, scientific decisions about AI risks and capabilities.

Our research tackles the hardest problems in agent robustness, AI control protocols, and AI risk evaluations to help governments, industry, and the public understand and mitigate AI risk while maximizing AI adoption. This team collaborates broadly across industry, the public sector, and academia and regularly publishes our findings. We are actively seeking talented researchers to join us in shaping this vision.

As a Research Scientist working on Agent Robustness you will work on the fundamental challenges of building AI agents that are safe and aligned with humans. For example, you might: 

• Research the science of AI agent capabilities with a focus on how they relate to safety, risk factors, and methodologies for benchmarking them;
• Design and build harnesses to test AI agents’ tendency to take harmful actions when pressured to do so by users or tricked into doing so by elements of their environment;
• Design and build exploits and mitigations for new and unique failure modes that arise as AI agents gain affordances like coding, web browsing, and computer use;
• Characterize and design mitigations for potential failure modes or broader risks of systems involving multiple interacting AI agents.

Ideally you’d have:

• Commitment to our mission of promoting safe, secure, and trustworthy AI deployments in the industry as frontier AI capabilities continue to advance.
• Practical experience conducting technical research collaboratively. You should be comfortable building and leveraging agent scaffolding, designing evaluation harnesses, and quickly turning new ideas from the research literature into working prototypes.
• Experience with post-training and RL techniques such as RLHF, DPO, GRPO, and similar approaches.
• A track record of published research in machine learning, particularly in generative AI.
• At least three years of experience addressing sophisticated ML problems, whether in a research setting or in product development.
• Strong written and verbal communication skills to operate in a cross-functional team.

Nice to have:

• Hands-on experience with agent evaluation frameworks such as SWE-bench, WebArena, OSWorld, Inspect, or similar tools.
• Experience with red-teaming, prompt injection, or adversarial testing of AI systems.

Our research interviews are crafted to assess candidates' skills in practical ML prototyping and debugging, their grasp of research concepts, and their alignment with our organizational culture. We will not ask any LeetCode-style questions. If you’re excited about advancing AI safety and contributing to our mission, we encourage you to apply, even if your experience doesn’t perfectly align with every requirement.

About Scale

At Scale AI, 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 abundance of frontier data to pave the road to Artificial General Intelligence (AGI), and building upon our prior model evaluation work with enterprise customers and governments, to deepen our capabilities and offerings for both public and private evaluations.

About the ACE team 

The Agent Capabilities & Environments (ACE) team, part of Scale’s Research organization, brings together customer-facing Researchers and Applied AI Engineers. Our core mission includes research on agent environments and RL reward signals, benchmarking autonomous agent performance across real-world scenarios and environments, creating robust data programs to improve Large Language Models (LLMs) agentic capabilities and building foundational tools and frameworks for evaluating models as agents. ACE focuses on autonomous agents that dynamically interact with diverse external environments, including code repositories, GUI interfaces, browsers, and more. 

About This Role

This role is at the intersection of cutting-edge AI research and practical application, with a focus on studying the data types essential for building state-of-the-art agents, such as browser and SWE agents. The ideal candidate will explore the data landscape needed to advance intelligent, adaptable AI agents, guiding the data strategy at Scale to drive innovation. This position requires not only expertise in LLM agents and planning algorithms but also creativity in addressing novel challenges related to data, interaction, and evaluation. You will contribute to impactful research publications on agents, collaborate with customer researchers, and work alongside the engineering team to translate these advancements into real-world, scalable solutions.

Ideally you’d have: 

• Practical experience working with LLMs, with proficiency in frameworks like Pytorch, Jax, or Tensorflow. You should also be adept at interpreting research literature and quickly turning new ideas into prototypes.
• A track record of published research in top ML venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, COLM, etc.)
• At least three years of experience addressing sophisticated ML problems, either in a research setting or product development.
• Strong written and verbal communication skills and the ability to operate cross-functionally. 

Nice to have:

• Hands-on experience with open source LLM fine-tuning or involvement in bespoke LLM fine-tuning projects using Pytorch/Jax. 
• Hands-on experience and publications in building applications and evaluations related to AI agents such as tool-use, text2SQL, browser agents, coding agents and GUI agents.
• Hands-on experience with agent frameworks such as OpenHands, Swarm, LangGraph, etc.
• Familiarity with agentic reasoning methods such as STaR and PLANSEARCH 
• Experience working with cloud technology stack (eg. AWS or GCP) and developing machine learning models in a cloud environment.

Our research interviews are crafted to assess candidates' skills in practical ML prototyping and debugging, their grasp of research concepts, and their alignment with our organizational culture. We will not ask any LeetCode-style questions.

Scale works with the industry’s leading AI labs to provide high quality data and accelerate progress in GenAI research. We are looking for Research Scientists and Research Engineers with expertise in LLM post-training (SFT, RLHF, reward modeling). This role will focus on optimizing data curation and eval to enhance LLM capabilities in both text and multimodal modalities.

In this role, you will develop novel methods to improve the alignment and generalization of large-scale generative models. You will collaborate with researchers and engineers to define best practices in data-driven AI development. You will also partner with top foundation model labs to provide both technical and strategic input on the development of the next generation of generative AI models.

You will:

• Research and develop novel post-training techniques, including SFT, RLHF, and reward modeling, to enhance LLM core capabilities in both text and multimodal modalities.
• Design and experiment new approaches to preference optimization.
• Analyze model behavior, identify weaknesses, and propose solutions for bias mitigation and model robustness.
• Publish research findings in top-tier AI conferences.

Ideally you’d have:

• Ph.D. or Master's degree in Computer Science, Machine Learning, AI, or a related field.
• Deep understanding of deep learning, reinforcement learning, and large-scale model fine-tuning.
• Experience with post-training techniques such as RLHF, preference modeling, or instruction tuning.
• Excellent written and verbal communication skills

• Published research in areas of machine learning at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) and/or journals
• Previous experience in a customer facing role.

As the leading data and evaluation partner for frontier AI companies, Scale is dedicated to advancing the evaluation and benchmarking of large language models (LLMs). We are building industry-leading LLM evals, setting new standards for model performance assessment. Our mission is to develop rigorous, scalable, and fair evaluation methodologies to drive the next generation of AI capabilities.

Our Research teams work with the industry’s leading AI labs to provide high quality data and accelerate progress in GenAI research. As the Tech Lead Manager of the LLM Evals Research team, you will lead a talented team of research scientists and research engineers focused on developing and implementing novel evaluation methodologies, metrics, and benchmarks to assess the capabilities and limitations of our cutting-edge LLMs. This role is critical for designing and executing a roadmap that defines best practices in data driven AI development and will accelerate the next generation of generative AI models in partnership with top foundational model labs. 

You will:

• Lead a team of highly effective research scientists and research engineers on LLM evals.
• Conduct research on the effectiveness and limitations of existing LLM evaluation techniques.
• Design and develop novel evaluation benchmarks for large language models, covering areas such as instruction following, factuality, robustness, and fairness. 
• Communicate, collaborate, and build relationships with clients and peer teams to facilitate cross-functional projects.
• Collaborate with internal teams and external partners to refine metrics and create standardized evaluation protocols.
• Implement scalable and reproducible evaluation pipelines using modern ML frameworks.
• Publish research findings in top-tier AI conferences and contribute to open-source benchmarking initiatives.
• Remain up-to-date on ongoing research in the team, help work through technical challenges, and be involved in design decisions
• Remain deeply involved in the research community, both understanding trends, and setting them
• Thrive in a high-energy, fast-paced startup environment and are ready to dedicate the time and effort needed to drive impactful results.

Ideally you'd have:

• 5+ years of hands-on experience in large language model, NLP, and Transformer modeling, in the setting of both research and engineering development
• Experience and track of recording in landing major research impacts in a fast-paced environment
• Experience supporting and leading a team of research scientists and research engineers
• Excellent written and verbal communication skills
• Published research in areas of machine learning at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) and/or journals
• Previous experience in a customer facing role.

Scale AI accelerates the development of AI systems by providing the data, infrastructure, and tooling that power the most advanced models in the world. Our teams operate at the intersection of cutting-edge research, large-scale engineering, and real-world deployment, partnering with leading frontier labs, enterprises, and government agencies to push Generative AI into new capabilities and applications.

As AI rapidly evolves from static models to dynamic, agentic systems, Scale is building the foundational research, evaluation methodologies, and agent/RL infrastructure that will define this next era. You’ll join a high-impact research organization driving advances in large-language models, post-training, evaluation, and agentic/RL environments, helping shape how next-generation AI is built, measured, and deployed.

As a Research Scientist Manager, you will lead a world-class team of research scientists and engineers, define the research roadmap, and drive execution from early prototyping to deployment. You’ll thrive in a fast-moving environment, balancing deep technical leadership with people management, vision setting, and delivery.

You will:

• Lead, mentor and grow a team of research scientists and engineers working on GenAI research initiatives (e.g., evaluation, post-training, agents, RL environments).  
• Define and drive a multi-year research roadmap: identify key scientific questions, set milestones, allocate resources, and ensure rigorous execution.
• Collaborate cross-functionally with engineering, product, client-facing teams and external academic or industry partners to translate research into components, insights, and actionable outcomes.
• Communicate compellingly: publish research, present at conferences, engage in open-source contributions, and represent the team externally.
• Drive an inclusive, high-performing culture: help your team through technical challenges, provide growth opportunities, and attract top talent.
• Stay deeply connected to the research community, understanding major trends, and helping set them.
• Thrive in a high-energy, fast-paced startup environment and are ready to dedicate the time and effort needed to drive impactful results.

Ideally you'd have:

• 5+ years of hands-on research experience (PhD or equivalent preferred) in machine learning, deep learning, generative models, agent/rl systems or related domains.
• A strong track record of research excellence, including publications in top-tier ML/AI venues (NeurIPS, ICML, ICLR, ACL, etc.).
• Experience and track of recording in landing major research impacts in a fast-paced environment
• Experience leading or managing research teams. You’re excited to mentor, coach and develop talent.
• Excellent written and verbal communication skills. You are able to articulate research ideas and outcomes to both technical and non-technical stakeholders.

As the leading data and evaluation partner for frontier AI companies, Scale is dedicated to advancing the evaluation and benchmarking of large language models (LLMs). We are building industry-leading LLM evals, setting new standards for model performance assessment. Our mission is to develop rigorous, scalable, and fair evaluation methodologies to drive the next generation of AI capabilities.

Our Research teams work with the industry’s leading AI labs to provide high quality data and accelerate progress in GenAI research. As a Staff Machine Learning Research Scientist on the LLM Evals team, you will lead the development of novel evaluation methodologies, metrics, and benchmarks to measure the capabilities and limitations of frontier LLMs. You will help define what "good" looks like in generative AI, driving research that informs both our internal roadmap and the broader research community. This role is critical for designing and executing a roadmap that defines best practices in data driven AI development and will accelerate the next generation of generative AI models in partnership with top foundational model labs.

You will:

• Drive research on the effectiveness and limitations of existing LLM evaluation techniques.
• Design and develop novel evaluation benchmarks for large language models, covering areas such as instruction following, factuality, robustness, and fairness. 
• Communicate, collaborate, and build relationships with clients and peer teams to facilitate cross-functional projects.
• Collaborate with internal teams and external partners to refine metrics and create standardized evaluation protocols.
• Implement scalable and reproducible evaluation pipelines using modern ML frameworks.
• Publish research findings in top-tier AI conferences and contribute to open-source benchmarking initiatives.
• Mentor and guide research scientists and engineers, providing technical leadership across cross-functional projects.
• Stay deeply engaged with the ML research community, tracking emerging work and contributing to the advancement of LLM evaluation science.
• Thrive in a high-energy, fast-paced startup environment and are ready to dedicate the time and effort needed to drive impactful results.

Ideally you'd have:

• 5+ years of hands-on experience in large language model, NLP, and Transformer modeling, in the setting of both research and engineering development
• Experience and track of recording in landing major research impacts in a fast-paced environment
• Experience tech leading a team of research scientists and research engineers
• Excellent written and verbal communication skills
• Published research in areas of machine learning at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) and/or journals
• Previous experience in a customer facing role.

About Scale

At Scale AI, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, fueling the most exciting advancements in AI, including generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re amplifying access to high-quality data to drive progress toward Artificial General Intelligence (AGI). Building on our history of model evaluation with enterprise and government customers, we are expanding our capabilities to set new standards for both public and private evaluations.

About This Role

This role operates at the forefront of AI research and real-world implementation, with a strong focus on reasoning within large language models (LLMs). The ideal candidate will study the data types critical for advancing LLM-based agents, including browser and software engineering (SWE) agents. You will play a key role in shaping Scale’s data strategy by identifying the most effective data sources and methodologies for improving LLM reasoning. Success in this role requires a deep understanding of LLMs, planning algorithms, and novel approaches to agentic reasoning, as well as creativity in tackling challenges related to data generation, model interaction, and evaluation. You will contribute to impactful research on language model reasoning, collaborate with external researchers, and work closely with engineering teams to bring state-of-the-art advancements into scalable, real-world solutions.

Ideally, you’d have:

• Practical experience working with LLMs, with proficiency in frameworks like PyTorch, JAX, or TensorFlow. You should also be skilled at rapidly interpreting research literature and turning new ideas into working prototypes.

• A track record of published research in top ML and NLP venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, CoLLM, etc.).

• At least three years of experience solving complex ML challenges, either in a research setting or product development, particularly in areas related to LLM capabilities and reasoning.

• Strong written and verbal communication skills, along with the ability to work effectively across teams.

Nice to have:

• Hands-on experience fine-tuning open-source LLMs or leading bespoke LLM fine-tuning projects using PyTorch/JAX.

• Research and practical experience in building applications and evaluations related to LLM-based agents, including tool-use, text-to-SQL, browser agents, coding agents, and GUI agents.

• Experience with agent frameworks such as OpenHands, Swarm, LangGraph, or similar.

• Familiarity with advanced agentic reasoning techniques such as STaR and PLANSEARCH.

• Proficiency in cloud-based ML development, with experience in AWS or GCP environments.

Our research interviews are designed to assess candidates' ability to prototype and debug ML models, their depth of understanding in research concepts, and their alignment with our organizational culture. We do not conduct LeetCode-style problem-solving assessments.

Scale Labs, Research Scientist — AI Controls and Monitoring

As the leading data and evaluation partner for frontier AI companies, Scale plays an integral role in understanding the capabilities and safeguarding AI models and systems. Building on this expertise, Scale Labs has launched a new team focused on policy research, to bridge the gap between AI research and global policymakers to make informed, scientific decisions about AI risks and capabilities.

Our research tackles the hardest problems in agent robustness, AI control protocols, and AI risk evaluations to help governments, industry, and the public understand and mitigate AI risk while maximizing AI adoption. This team collaborates broadly across industry, the public sector, and academia and regularly publishes our findings. We are actively seeking talented researchers to join us in shaping this vision.

As a Research Scientist focused on AI Controls and Monitoring, you will design methods, systems, and experiments to ensure that advanced AI models and agents remain aligned with intended goals, even in high-stakes or adversarial environments. For example, you might:

• Develop monitoring techniques and observability methods that track AI behavior in real time to identify and flag deviations, emergent capabilities, or anomalous outputs;
• Research mechanisms for layered control, including fail-safes, oversight protocols, and intervention methods that can halt or redirect AI systems when risks are detected;
• Design red-team simulations to probe weaknesses in oversight and control mechanisms, and build mitigations to close identified gaps;
• Collaborate with policymakers, engineers, and other researchers to establish standards and benchmarks for AI monitoring and escalation.

Ideally you’d have:

• Commitment to our mission of promoting safe, secure, and trustworthy AI deployments in the industry as frontier AI capabilities continue to advance.
• Practical experience conducting technical research collaboratively. You should be comfortable designing control and monitoring experiments for AI systems, building prototype systems, and quickly turning new ideas from the research literature into working prototypes.
• A track record of published research in machine learning, particularly in generative AI.
• At least three years of experience addressing sophisticated ML problems, whether in a research setting or in product development.
• Strong written and verbal communication skills to operate in a cross-functional team.

Nice to have:

• Experience with runtime monitoring, anomaly detection, or observability for ML systems.
• Familiarity with AI control or alignment research (e.g., scalable oversight, interpretability, debate).
• Experience with post-training and RL techniques such as RLHF, DPO, GRPO, and similar approaches.

Our research interviews are crafted to assess candidates' skills in practical ML prototyping and debugging, their grasp of research concepts, and their alignment with our organizational culture. We will not ask any LeetCode-style questions. If you’re excited about advancing AI safety and contributing to our mission, we encourage you to apply, even if your experience doesn’t perfectly align with every requirement.

At Scale AI, we help leading enterprises turn AI from a promising capability into reliable systems that improve real workflows and deliver measurable business value. We are hiring a Senior Forward Deployed Data Scientist / Engineer to work directly with customers on ambiguous, high-impact problems at the intersection of data science, product development, and AI deployment.

This is not a traditional analytics role. On this team, data scientists do the core statistical and modeling work, but they also build real tools and products: evaluation explorers, operator workflows, decision-support systems, experimentation surfaces, and customer-specific AI/data applications that get used in production. In many cases, the data scientist builds the first usable version of the solution, proves value quickly, and helps drive it into a durable product or platform capability.

The right candidate is strong in first-principles problem solving, rigorous measurement, and technical execution. They know how to define metrics, design experiments, diagnose failures, and build systems that people actually use. They are also comfortable using modern AI-assisted development tools to prototype and iterate quickly without sacrificing reliability, observability, or judgment. Python and SQL matter in this role, but as execution fluency in service of building better products and making better decisions.

What you’ll do

• Partner directly with enterprise customers to understand workflows, operational pain points, constraints, and success criteria
• Turn ambiguous business and product problems into measurable solutions with clear metrics, technical designs, and deployment plans
• Design and build internal and customer-facing data products, including evaluation tools, workflow applications, decision-support systems, and thin product layers on top of data/ML systems
• Build end-to-end solutions across data ingestion, transformation, experimentation, statistical modeling, deployment, monitoring, and iteration
• Design evaluation frameworks, benchmarks, and feedback loops for ML/LLM systems, human-in-the-loop workflows, and model-assisted operations
• Apply rigorous statistical thinking to experimentation, causal inference, metric design, forecasting, segmentation, diagnostics, and performance measurement
• Use AI-assisted development workflows to accelerate prototyping and product iteration, while maintaining strong engineering discipline
• Diagnose failure modes across data quality, model behavior, retrieval, workflow design, and user experience, and drive fixes into production
• Act as the voice of the customer to Product, Engineering, and Data Science, using field learnings to shape roadmap and platform capabilities

What we’re looking for

• 5+ years of experience in data science, machine learning, quantitative engineering, or another highly analytical technical role
• Proven track record of shipping data, ML, or AI systems that delivered measurable business or product impact
• Exceptional ability to structure ambiguous problems, define the right success metrics, and translate them into executable technical plans
• Strong foundation in statistics, experimentation, causal reasoning, and measurement
• Experience building tools or products, not just analyses — for example internal workflow tools, evaluation systems, operator-facing products, experimentation platforms, or customer-specific applications
• Hands-on fluency in Python, SQL, and modern data/AI tooling; able to inspect data, prototype quickly, debug deeply, and productionize solutions that work
• Comfort using AI-assisted coding and development workflows to move from idea to usable product quickly
• Strong communication and stakeholder management skills; able to work effectively with customers, engineers, product teams, and executives
• High ownership and bias toward shipping in fast-moving environments with incomplete information

Preferred qualifications

• Experience in a forward deployed, solutions, consulting, or other client-facing technical role
• Experience designing evaluation frameworks for LLMs, retrieval systems, agentic workflows, or other AI-enabled products
• Experience with large-scale data processing and distributed systems such as Spark, Ray, or Airflow
• Experience with cloud infrastructure and modern data platforms such as AWS, GCP, Snowflake, or BigQuery
• Experience building lightweight applications, APIs, internal tools, or workflow software on top of data/ML systems
• Familiarity with marketplace experimentation, causal inference, forecasting, optimization, or advanced statistical modeling
• Strong product instinct and the judgment to know when the right answer is a model, an experiment, a tool, or a workflow redesign

What success looks like

Success in this role means taking a messy, high-stakes customer problem and turning it into a deployed system that is actually used. Sometimes that system is a model. Sometimes it is an evaluation framework. Sometimes it is an operator-facing tool or a lightweight data product that changes how decisions get made. In all cases, success is defined by measurable impact, rigorous evaluation, and reliable execution.

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

The mission of MongoDB Research is to advance the state of the art in database systems by conducting fundamental and applied research that directly impacts MongoDB's products. Our goal is to foster innovation that benefits MongoDB by addressing critical challenges in data management, performance optimization, security, scalability, and usability. Through collaboration, publication, and active engagement with leading research institutions, we strive to shape the future of database technology and ensure MongoDB remains at the forefront of innovation.

We seek an exceptional researcher specializing in database systems with particular expertise in database architecture, including query optimization, storage engines, indexing, and multicore processing. The ideal candidate will demonstrate a strong track record in designing and implementing highly performant database systems.

This role can be in-office, flexible, or based remotely in North America.

Responsibilities:

• Conduct innovative research in query optimization, indexing methods, storage engines, and performance optimization
• Design, prototype, and empirically evaluate novel database systems and architectures that leverage modern hardware features, such as SIMD parallelism, GPU acceleration, and multicore processing
• Collaborate closely with our Engineering teams to translate cutting-edge research into practical solutions applicable in commercial database products
• Publish influential research outcomes in top-tier database and systems conferences and journals (e.g., VLDB, SIGMOD)
• Build a Database Systems research group with a critical mass of 2-3 researchers over the next 1-2 years
• Mentor junior researchers, fostering a collaborative and productive research environment

Qualifications:

• Ph.D. in Computer Science, Computer Engineering, or a closely related field with a specialization in database systems
• 12+ years of experience applying computer science concepts to large scale software development problems, including databases and distributed systems
• Proven track record of impactful research publications in top-tier database and systems conferences and journals
• Demonstrated expertise in query optimization, efficient data structures, and high-performance database architectures
• Strong background in performance engineering, hardware-aware optimizations, and database engine internals
• Experience in building research prototypes and conducting rigorous empirical studies to validate theoretical models
• Excellent analytical, communication, and interpersonal skills, with the ability to collaborate across teams

Success metrics:

• Within the first three months, you will have established working relationships with key stakeholders across Product and Engineering
• Within the first six months, you will have completed a review of the company’s database roadmap and identified opportunities for research impact. You will have established a clear research agenda for the team
• Within the first twelve months, you will have successfully attracted and interviewed researchers to join the team 

About MongoDB

MongoDB is built for change, empowering our customers and our people to innovate at the speed of the market. We have redefined the database for the AI era, enabling innovators to create, transform, and disrupt industries with software. MongoDB’s unified database platform—the most widely available, globally distributed database on the market—helps organizations modernize legacy workloads, embrace innovation, and unleash AI. Our cloud-native platform, MongoDB Atlas, is the only globally distributed, multi-cloud database and is available across AWS, Google Cloud, and Microsoft Azure.

With offices worldwide and nearly 60,000 customers—including 75% of the Fortune 100 and AI-native startups—relying on MongoDB for their most important applications, we’re powering the next era of software.

Our compass at MongoDB is our Leadership Commitment, guiding how and why we make decisions, show up for each other, and win. It’s what makes us MongoDB. 

To drive the personal growth and business impact of our employees, we’re committed to developing a supportive and enriching culture for everyone. From employee affinity groups, to fertility assistance and a generous parental leave policy, we value our employees’ wellbeing and want to support them along every step of their professional and personal journeys. Learn more about what it’s like to work at MongoDB, and help us make an impact on the world!

MongoDB is committed to providing any necessary accommodations for individuals with disabilities within our application and interview process. To request an accommodation due to a disability, please inform your recruiter.

MongoDB, Inc. provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type and makes all hiring decisions 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.

Req ID: 1263126935

Staff GenAI Research Scientist - Retrieval

P-1214

At Databricks, we are obsessed with enabling data teams to solve the world’s toughest problems, from security threat detection to cancer drug development. We do this by building and running the world’s best data and AI platform, so our customers can focus on the high value challenges that are central to their own missions.

The Databricks AI Research organization enables companies to develop AI models and systems using their own data, with technologies ranging from pre-training LLMs from scratch to augmented generation using the latest retrieval techniques. Databricks AI does so by producing novel science and putting it into production. Databricks AI is committed to the belief that a company’s AI models are just as valuable as any other core IP, and that high-quality AI models should be available to all.

Job Description 

As a Staff Research Scientist on the Enterprise Agents team, you will be responsible for keeping up with the latest developments in Agent Context, Information Retrieval, Knowledge Assistants, RAG, Embeddings, and advancing the scientific frontier by creating new techniques that go beyond the state of the art. You will work together on a collaborative team of researchers and engineers with diverse backgrounds and technical training. And most importantly, you will love our customers: our goal is to make our customers successful in applying state-of-the-art LLMs and AI systems, and we encode our scientific expertise into our products to make that possible.

The Impact you will have

As a Staff Research Scientist on the AI Research Team at Databricks, You Will: 

• Define and lead independent research agendas on Information Retrieval for reusable Agent components, conducting experiments to empirically validate hypotheses and benchmark against state-of-the-art approaches.
• Drive research and technical strategy across enterprise retrieval and search, including workspace search, vector search.
• Drive algorithmic innovations for Retrieval within Large Language Models. 
• Experience with Agent Benchmarks for retrieval, text-embeddings and LLM Data Quality. 

What We Look for

• 5+ years of 
• PhD in Computer Science or related field with strong foundations in Natural Language Processing and Information Retrieval 
• Developing and implementing methods that extend and improve model capabilities, reliability, and safety. 
• Strong preference for candidates with first-author publications at top ML/systems conferences (EMNLP, ICLR, ACL, NeurIPs) focused on optimization or efficiency.

What You’ll Be Part Of:

An iSpot Research Data Scientist is a key contributor to the future growth of the company, pushing boundaries on what is measurable in the TV viewing and advertising space. The Research Data Science team builds innovative solutions for iSpot’s audience measures, attribution and lift analytics, and creative testing. After developing new methodologies and building prototypes, we work with our product and engineering teams to scale our models to satisfy the needs of brands, publishers, networks, and agencies in a constantly evolving marketing landscape.

Responsibilities:

Qualifications and Education Requirements:

Preferred Skills:

Target cash compensation range: $95,950 - $106,000 USD Annually

We are committed to providing competitive, market-informed compensation. The cash compensation above includes base salary, variable commission for employees in eligible roles, and annual bonus targets for eligible roles. In addition to cash compensation, all full time iSpotters are eligible to participate in iSpot’s equity plan to receive stock options. Non-exempt roles will also be eligible for (pre-approved) overtime pay. Individual compensation packages are influenced by different factors unique to each candidate, including their skills, experience, qualifications and other job-related reasons.

For more information on total rewards package, go HERE

Hybrid & Flexible Workplace Policy

iSpot supports a hybrid and flexible workplace. Depending on location and work responsibilities, employees may be designated as full-time or part-time office-based or a fully remote employee. A hybrid work schedule indicates that you work in the office some days and work from home other days. The best hybrid workplaces allow for flexibility while also encouraging consistency. 

Those local or living in surrounding areas to one of our offices (Bellevue, WA or New York, NY) will work a hybrid schedule, coming into their local office 1-3 days a week. While those in a role, not office-based and located further away from our offices, will work a fully remote schedule. If you have questions regarding exact details of our hybrid & flexible workplace policy, please let your recruiter know and they will discuss with you further.

#LI-Remote

Hex's user base is continuing to grow rapidly. We are hiring a Sales Engineer to partner with potential customers to support their Hex evaluations.

About the role

Hex is redefining how teams work with data - combining notebooks, apps, BI, and AI-native workflows into a single collaborative analytics platform. Companies like Anthropic, Cursor, and Brex rely on Hex as a core part of how their data teams operate.

We’re at an exciting stage of growth. Hex is scaling rapidly, with strong momentum in the mid-market and SMB segments. This is a high-impact moment to join: our go-to-market motion is accelerating, our AI capabilities are evolving quickly, and Sales Engineers play a central role in shaping how we win.

As a Sales Engineer, you will leverage your product, ecosystem, and technical expertise to shape how our potential customers evaluate and adopt Hex. You will partner with the Sales team to help prospects understand the challenges in their current state, connect Hex’s value to those challenges, own the technical evaluation, and set them up for long-term success as a customer. This is a consultative, highly technical position where you’ll influence deal strategy, inform product direction, and act as the voice of the customer.

If you want to learn more about the team, what we do, and how we work, please check out our team page!

What you will do

• Serve as a trusted technical advisor to data teams (think: Data Scientist or Analyst writing SQL and Python) and build strong relationships to help them get the most out of Hex during their evaluations
• Own the technical sales cycle and work with end users throughout the Sales cycle from discovery and product demos through trials, and proofs of concept
• Partner closely with Account Executives to develop opportunity-level strategy, execute evaluations, and share feedback to improve your partnership
• Inform the product roadmap by synthesizing product feedback from prospects to share with internal Engineering & Product teams
• Project manage the architecture, security, and technical reviews that prospects require
• Develop content to support users who are a part of evaluations ie. demo projects, documentation, videos, etc.
• As needed, travel to customer on-sites and marketing events to deliver demos and build strong customer relationships

Who you might be

You might be a fit for this role if you have:

• 5+ years of professional work experience
• 3+ years in a Sales Engineer, Solutions Engineer, or similar customer-facing technical role
• Experience working with Mid Market customers (250-2500 employees)
• Experience with land POC/Technical Sales Motions
• Familiarity with data science tooling and workflows, including Python, SQL, and BI tools (Bonus points if you have spent lots of time in notebooks, SQL IDEs, or app-building frameworks.)
• A strong track record of talking to customers - from IC users to senior stakeholders - and solving their problems
• You're an excellent communicator who builds empathy, shares feedback, and grows successful relationships with internal and external stakeholders
• A generalist technical skillset: you don't need a CS degree, but should have a passion and interest for technology and be a creative problem solver
• Experience or interest in working in an early-stage startup environment

In addition to our unique culture, Hex proudly offers a competitive total rewards package, including but not limited to, market-benched salary & equity, comprehensive health benefits, and flexible paid time off.

The OTE (base + variable) range for this role is: $175,000 - $200,000.

The salary range shown may be a reflection of additional factors such as geographical location and skill ranges/levels we’re open to. Placement in the salary range will be decided upon completion of the interview process, taking into account factors like leaving room for growth, internal fairness & parity, your demonstrated skills, and the depth of your experience. Our Recruiting team will be able to provide more details during the process.

By submitting an application the candidate consents to the use of their personal information in accordance with the Hex Privacy policy: https://learn.hex.tech/docs/trust/privacy-policy.

Verana Health, a digital health company that delivers quality drug lifecycle and medical practice insights from an exclusive real-world data network, recently secured a $150 million Series E led by Johnson & Johnson Innovation – JJDC, Inc. (JJDC) and Novo Growth, the growth-stage investment arm of Novo Holdings. 

Existing Verana Health investors GV (formerly Google Ventures), Casdin Capital, and Brook Byers also joined the round, as well as notable new investors, including the Merck Global Health Innovation Fund, THVC, and Breyer Capital.

We are driven to create quality real-world data in ophthalmology, neurology, urology, and oncology to accelerate quality insights across the drug lifecycle and within medical practices. Additionally, we are driven to advance the quality of care and quality of life for patients. DRIVE defines our internal purpose and is the galvanizing force that helps ground us in a shared corporate culture. DRIVE is: Diversity, Responsibility, Integrity, Voice-of-Customer and End-Results. Click here to read more about our culture and values.

Our headquarters are located in San Francisco and we have additional offices in Knoxville, TN and New York City with employees working remotely in AZ, CA, CO, CT, FL, GA, IL, LA, MA, MN, NC, NJ, NV, NY, OH, PA, SC, TN, TX, VA, WA, D.C. All employees are required to have permanent residency in one of these states. Candidates who are willing to relocate are also encouraged to apply. 

Candidates based in the Bay Area, New York City, and Knoxville, Tennessee will be give priority consideration. For these locations, the position will follow a hybrid work model, with 3 days per week in office and the remaining days working from home. 

*Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.*

Job Title: Quantitative Scientist

Why This Role Matters:

The Quantitative Scientist will be responsible for executing and providing oversight and guidance on the analytical, statistical and epidemiological components of Verana research projects and data quality initiatives. The primary function of this role will be to conduct, guide, and manage the analytical execution of Verana research projects . The Quantitative Scientist will collaborate cross-functionally to build research capacity and adopt quality standards across Verana's business units.  This position requires an experienced individual with knowledge of statistics, ability to develop and review code with a solid understanding of coding best practices, a strong understanding of epidemiological study designs, as well as excellent communication and interpersonal skills.  In addition, you should be able to continually unearth and present interesting trends that have clinical, research, and business importance for our customers and oncology patients at large.  You will be joining a friendly, high performing and diverse research team with backgrounds in public health, health economics, biology, and statistics.  This role will report to the Vice President, Research.

WHAT WILL YOU GAIN

• Expertise in the oncology real-world data industry, a fast-growing segment of health data
• Experience executing oncology research and analytical/statistical methods to answer key questions for patients, providers, and life science companies

WHAT TO EXPECT IN YOUR FIRST YEAR AT VERANA:

In thirty days, you will:

• Understand the full suite of Verana’s research offerings and analyses
• Understand the research team’s GitHub repo and analysis template 

In three months, you will:

• Contribute to internal and external meetings related to Verana research and analyses
• Gain expert understanding of Verana’s data model  
• Implement clinical specifications on real-world data and add to the suite of functions available for analyses

In six months, you will:

• Personally execute analytical research projects
• Contribute to the development of project collateral, research protocols, abstracts, posters and manuscripts
• Guide and mentor data scientists and analysts 

In one year, you will:

• Lead the analytical execution of Verana’s research projects for life science, provider, and regulatory partners 
• Scope projects to determine feasibility, estimated costs, and expected timelines
• Provide guidance to Verana’s client partners in the use of Verana’s data model and analytical/statistical methodologies 

HOW YOU WILL IMPACT VERANA

• Lead the design and development of Verana research studies from a statistical and analytical perspective
• Contribute to the development of protocols and statistical analysis plans 
• Develop code and produce results as defined in protocols and statistical analysis plans
• Collaborate with physicians, statisticians, data scientists, business leaders, and others within and outside Verana to conduct research projects including observational studies, pharmacoeconomic analyses, and other relevant study types
• In partnership with the Research Team, communicate studies in progress and demonstrate results to key stakeholders through high quality abstracts, posters, and publications
• Manage and mentor a team of data analysts and data scientists, including reviewing code and analytical output
• Contribute to the evolution of Verana’s quality assurance framework, including identifying data quality issues during analysis
• Maintain current and in-depth understanding of real-world data analytical methodologies, along with the ability to lead and mentor internal team members in successfully executing them

WHAT YOU BRING TO THE TABLE

• Masters degree in computer science, statistics, applied math, econometrics, MPH, or similar quantitative field
• 3+ years of relevant research experience in the field of oncology (post-graduation)
• Experience working with real-world data sourced from electronic health records
• Experience analyzing claims data 
• Advanced knowledge of SQL, R programming, and version control (git)
• Deep understanding and experience with descriptive statistics, logistic and multivariate regression analysis, power calculations, propensity score analysis, and survival analysis
• Significant experience developing protocol documents, statistical analysis plans (SAPs), and manuscript tables and figures
• Evidences well-developed conceptual and integrative thinking with ability to access internal and external resources as needed 
• Project implementation and execution, including engagement with key stakeholders, with a high degree of autonomy
• Mentors junior analysts in HEOR and statistical analysis methodologies
• Excellent written and verbal communication skills
• Strong organizational and time management skills 
• Works well independently, as a team member, and as a team leader 
• Comfort working in a fast-paced, dynamic environment

NICE TO HAVE

• Experience implementing data science methodologies
• Understanding of data quality metrics and/or quality management systems

Benefits:

• We provide 100% health, vision, and dental coverage for employees 
• 401K Match
• Flexible vacation plans 
• $700 learning and wellness annual stipend 
• Headspace meditation app access

Final note:

You do not need to match every listed expectation to apply for this position. Here at Verana, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.

Caution to Job Applicants: Be vigilant against potential scams. Verana Health will never ask for payment or personal information upfront. Verify company details, cross-check job offers, and trust your instincts. Any legitimate job offer will be received by a Verana Health email account (not via gmail, text or other means) Report suspicious activities to protect yourself and others in the job-seeking community.

#LI-REMOTE

Layer Health was founded in 2023 by leading machine learning researchers from MIT and Harvard Medical School. We are building an AI layer that can accurately and scalably synthesize information from medical records, with the mission to reduce friction everywhere in healthcare. Our LLM-powered platform is solving chart review once and for all, across use cases. For health systems, our first product dramatically accelerates clinical registry abstraction in areas ranging from surgery and cardiology, to oncology. Our long term vision is for our AI layer to safely transform patient care and minimize unnecessary heartbreak. Layer Health’s diverse founding team brings expertise across machine learning, UI/UX, large language models, and medicine.

Here’s a collection of articles about our product, mission, recent funding round, etc.

Job Description

You will work with the Layer Health ML team applying our existing ML workflow to tailor models for a specific clinical use case. You will design, build, evaluate, and iterate on these models — combining strong data science fundamentals with modern LLM techniques to deliver high-accuracy, production-ready solutions across new clinical domains.

This is a high-velocity, high-ownership role for someone who enjoys diving deep into error modes, making targeted improvements, and closing the loop between model performance and real-world impact.

This contract will require full-time hours.

What you’ll do:

• Own end-to-end development of models for specific use cases: Build and iterate using our ML/LLM-powered workflows for new clinical areas, from initial prototyping through production refinement.
• Conduct evaluation & error analysis: Own rigorous evaluation of model performance; perform deep error analysis to identify systematic failure modes and drive targeted improvements.
• Ship frequent, high-quality updates to models based on data, feedback, and observed edge cases.
• Leverage LLM application & tooling: Apply modern LLM techniques (prompting strategies, structured outputs, tool use, eval frameworks) to improve accuracy and robustness.
• Support analytics & metrics: Contribute to metrics, reporting, and internal dashboards that track model performance and downstream impact.

We look for:

• Good data science fundamentals, including knowledge of best practices for evaluating model performance and error analysis. (A proven track record of successful projects in healthcare or clinical applications is a bonus, but not required.)
• Familiarity with modern applied LLM techniques and their practical implementations, for example e.g. standard prompting techniques, LLM frameworks, structured output usage, etc. (any experience using these techniques is a bonus).
• Solid programming skills in Python.
• A strong technical communicator who thrives in a fast-paced environment.
• Proactive mindset to identify and solve problems, continuously improving our data science capabilities.
• An excited and adaptable team player who wants to disrupt the healthcare industry with AI/ML, alongside an awesome team. 
• Willingness to be hybrid with our team in either our Boston or NYC office 3 days per week.

Expected compensation range for this role is $40 - $70/hour. Compensation is dependent on experience, overall fit to our role, and candidate location. Expected compensation ranges for this role may change over time. If your compensation requirement is greater than our posted salary ranges, please still consider applying to our role. We will make a determination as to whether an exception can be made. 

If you are excited about this role, we encourage you to apply even if you don't feel that you meet every single requirement. We're eager to meet people that believe in our mission and can contribute to our team in a variety of ways. We welcome diverse perspectives, rigorous thinking, and fearlessness in challenging the status quo. 

Join us and help us transform healthcare with AI. Layer Health is committed to foster an environment of inclusion that is free from discrimination.  We are an Equal Opportunity Employer where employment is decided on the basis of qualifications, merit, and business need. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected Veteran status, or any other characteristic protected by law. 

About the Role

The New York Times is a technology company committed to producing the world's most reliable and highest quality journalism. Our ability to do so relies on a talented team of expert technologists who help NYT learn from a tremendous abundance of data unique to this company.

The Algorithmic Recommendations and Audience Data Science team aims to help users discover content across the Times' website, apps and emails by applying algorithms that make use of information about our readers' behavior and our editorial judgment. We also build internal tools for the newsroom to better understand story performance and coverage trends. We are a group of machine learning scientists and data analysts that partner with teams across The New York Times. We are looking for a Machine Learning Scientist to join the team and apply machine learning methods to meet this challenge. You will report to a Lead Machine Learning Scientist, and collaborate with partners across the company.

Responsibilities:

• You will reframe business and newsroom goals as machine learning tasks that deliver accurate predictions, relevant insights, and optimization

• You will adapt or develop machine learning algorithms in cases when existing algorithms are insufficient, while implementing simple approaches when appropriate

• You will implement and deploy machine learning research with robustness and reproducibility, with consideration of risks and trade-offs

• You will turn models into data products, collaborate with engineering teams, and integrate into processes throughout The Times

• You will communicate complex ideas in machine learning while collaborating with technical and non-technical colleagues in in engineering, analytics, product management, marketing, editorial, and executive leadership groups

• Demonstrate support and understanding of our value of journalistic independence and a commitment to our mission to seek the truth and help people understand the world.

Basic Qualifications:

• PhD, MS + 2 years experience, or 3+ years work experience in machine learning, statistics, computational social science, applied mathematics, or another quantitative/computational discipline

• 2+ years experience with open source machine learning or statistical analysis tools

• 2+ years coding experience in Python

• 2+ years experience in SQL and manipulating large structured or unstructured datasets for analysis

Preferred Qualifications:

• 1+ year of experience with recommendation systems, using natural language processing and small and large language models.

• 1+ years of experience with deep learning architectures, fine-tuning, embeddings and Pytorch or Tensorflow

• 1+ years of experience translating ambiguous business questions into machine learning problems

• 1+ years of experience building data products, either internal or consumer-facing

REQ-020029

About the Role, Mission or Department Overview

The New York Times is a technology company committed to producing the world's most reliable and highest quality journalism. Our ability to do so relies on a talented team of expert technologists who help NYT learn from a tremendous abundance of data unique to this company.

The Algorithmic Targeting team (ALTA) is expanding its focus to promote subscriber growth through prescriptive machine learning (ML) on paid and owned media surfaces. We use methodologies like causal ML, inverse gap weighting and hierarchical bayesian modeling and deploy them at scale.

We are looking for a Machine Learning Scientist to join ALTA and contribute to all steps of the ML development and deployment cycle. You will lead the development of models that improve our marketing spend across external platforms and owned channels. You will report to our Director of Machine Learning.

Responsibilities:

• You will reframe business goals as ML tasks for delivering relevant insights, accurate predictions, and optimization
• You will implement and deploy machine learning research with robustness and reproducibility, with consideration of risks and trade-offs
• You will communicate complex ideas in machine learning while collaborating with colleagues in in engineering, analytics, product management, marketing, editorial, and executive leadership groups
• You will deploy models behind production APIs or batch processes, collaborate with engineering teams, and integrate into processes throughout The Times
• You will learn new technologies and ML methods, and adapt them into our workflows
• Demonstrate support and understanding of our value of journalistic independence and a strong commitment to our mission to seek the truth and help people understand the world.

Basic Qualifications:

• PhD, MS + 2 years experience, or 3+ years experience in statistics, computational social science, applied mathematics, economics, or another quantitative/computational discipline
• 2+ years experience with open source machine learning or statistical analysis tools
• 2+ years coding experience in Python
• 1+ year experience in SQL and manipulating large structured or unstructured datasets for analysis
• 1+ year experience collaborating with multiple developers on a shared codebase

Preferred Qualifications:

• 1+ years of experience building statistical and causal inference models
• Familiarity with digital marketing concepts like Cost Per Order (CPO), Lifetime Value (LTV), and the marketing funnel
• Familiarity with experimental design and randomized control trials
• Familiarity with prescriptive machine learning
• Familiarity with modern methods for Marketing Mix Modeling (MMMs)

REQ-019981

Hi, we're Oscar. We're hiring a Senior Data Scientist to join our Data Team. 

Oscar is the first health insurance company built around a full stack technology platform and a relentless focus on serving our members. We started Oscar in 2012 to create the kind of health insurance company we would want for ourselves—one that behaves like a doctor in the family.

About the role:

We are a Data Science team at Oscar dedicated to improving the Member Experience by embedding advanced analytics into our core technology and operations. We focus on building data products that bridge the gap between complex health insurance workflows and seamless, member-centric service.

You will drive the evolution of our member-facing tools by developing the LLM frameworks and ML models that underpin our automated support systems and digital interfaces. You will be responsible for evaluating both tool performance and member behavior to surface deep insights that inform our broader product and operations strategy.

You will report into the Lead Data Scientist.

Work Location: This position is based in our New York City office, requiring a hybrid work schedule with 3 days of in-office work per week. Thursdays are a required in-office day for team meetings and events, while your other two office days are flexible to suit your schedule. #LI-Hybrid

Pay Transparency: The base pay for this role is: $163,944 - $215,176.50 per year. You are also eligible for employee benefits, participation in Oscar's unlimited vacation program, company equity grants and annual performance bonuses.

Responsibilities:

• Works with team and manager to impact longer term strategies and roadmap
• Sets shorter term roadmap for team
• May manage data scientists and/or analysts
• Interacts and collaborates closely with data and business counterparts across functional areas
• Researches, develops, and maintains data pipelines, statistical/ML models, and/or advanced analyses
• Compliance with all applicable laws and regulations
• Other duties as assigned 

Requirements:

• 4+ years of industry or other quantitative technical fields (which may include academia).
• 3+ years of work experience working with SQL, R, and/or Python to query, manipulate and analyze data
• 3+ years experience building data models, using more advanced analytics methods, statistical modeling, and/or data processing

Bonus points:

• Advanced degree in a quantitative or technical field
• Experience in the healthcare, finance and/or insurance industries
• Experience evaluating consumer-facing digital products in collaboration with Product and Engineering teams

The role

At HelloFresh, our mission is to change the way people eat - forever. From our 2011 founding in Europe’s vibrant tech hub Berlin, we’ve become the global market leader in the meal kit sector and inspire millions of energised home cooks across the globe every week.

Our operation planning team, located in Berlin and New York, is critical to what we do. From operation and supply chain optimization to forecasting, we work on challenging problems and have a high output of building and releasing features and engines that make our business thrive and deliver real financial impact.

As an Applied Scientist in Operations Researcher (OR), you will work together with a global, cross-functional team that is transforming supply chain management, from menu planning and procurement to production and logistics. In this role, you will play a vital part in enhancing and expanding our algorithmic planning capabilities. Your contributions will directly impact the efficiency and performance of our business operations.

What you’ll do

• Design, implement and evolve optimization models and decision-support tools for high-impact planning problems in the supply chain (e.g. assortment/menu structure, capacity, sourcing, fulfillment and network trade-offs, priorities will follow business needs).
• Drive adoption of globally developed optimization capabilities: localize assumptions, validate outputs against operational reality and coordinate staged roll-outs with stakeholders and engineering.
• Partner with engineering to productionize solvers, services and workflows; ensure ownership, runbooks, and monitoring are clear after launch.
• Build and maintain data pipelines and, where useful, dashboards so inputs, outputs, and KPIs stay transparent and we can iterate with measurable feedback.
• Spot gaps early: translate messy reality into robust, data-backed formulations and pragmatic solutions that hold up at scale.
• Collaborate across product, engineering, data, and commercial teams; represent the US planning perspective in global OR discussions so shared roadmaps stay fit for purpose.
• Champion a shared, optimization-aware, data-driven way of planning — without losing sight of simplicity and trust from business users.

What you’ll bring

• Master’s or PhD in Operations Research, Mathematics, Industrial Engineering, Management Science, or a closely related field.
• Deep knowledge of mathematical optimization and core OR methods (e.g. mixed-integer programming, decomposition and sound use of heuristics where appropriate).
• Hands-on experience with industrial solvers such as Gurobi, CPLEX, or comparable (commercial or open-source).
• Senior-level track record: Either 5+ years of relevant professional experience applying OR/optimization in industry, or a PhD plus 3+ years of post-PhD applied work, demonstrating shipped models or tools and clear ownership of outcomes.
• Strong Python and SQL for modelling, experimentation, and working with large operational datasets; familiarity with BI tools (e.g. Tableau) is a plus.
• Excellent written and verbal communication; you can explain trade-offs, build trust, and align priorities across levels.
• Autonomy and judgment: you prioritise under ambiguity, run several meaningful threads in parallel, and keep quality high as scope grows.

You’ll get…

• Competitive hourly rate, 401K company match that vests immediately upon participation, & team bonus opportunities
• Generous PTO and flexible attendance policy 
• Comprehensive health and wellness benefits with options at $0 monthly, effective first day of employment
• Up to 85% discount on subscriptions to HelloFresh meal plans (HelloFresh, Green Chef, Everyplate, and Factor_)   
• Access to Employee Resource Groups that are open to all employees, including those pertaining to BIPOC, women, veterans, parents, and LGBTQ+
• Inclusive, collaborative, and dynamic work environment within a fast-paced, mission-driven company that is disrupting the traditional food supply chain

This job description is intended to provide a general overview of the responsibilities. However, the Company reserves the right to adjust, modify, or reassign work tasks and responsibilities as needed to meet changing business needs, operational requirements, or other factors.

Octus delivers breaking news and market-moving intelligence through cutting-edge data and technology for hedge funds, investment banks, and law firms- and we’re transforming how professionals access complex and opaque information. As part of our high-performing AI Innovation team, you’ll help design, build, and productionize modern GenAI and LLM-powered systems that support both client-facing features and internal operational efficiency. You’ll work end to end—from shaping ambiguous problems into scalable solutions to deploying reliable AI models in production - collaborating closely with product, engineering, and infrastructure teams. This is a hybrid role based in NYC (3 days in office per week). Curious about what we’re building? Check out our flagship GenAI product, CreditAI here and our AI framework here.

Responsibilities

• Apply strong problem-solving and critical thinking skills to break down complex, ambiguous requirements into clear, implementable technical components and system designs.
• Design, build, and maintain AI-powered and data-driven systems with a focus on modern language and multimodal models, including LLM-driven applications, RAG pipelines, and agentic workflows.
• Evaluate and productionize commercial and open-source LLMs, choosing appropriate models, tools, and techniques for each use case. Develop multi-step agentic workflows that incorporate tools, external data sources, memory, and control logic.
• Manage the orchestration of production LLM workflows and agentic systems, ensuring reliability and efficiency through prompt routing, state management, retries, fallbacks, and error handling. Design, test, and iteratively refine prompts and system instructions using prompt engineering and tuning techniques to improve model reliability, accuracy, and task performance.
• Maintain production-grade code and services with automated monitoring and performance tracking, using metrics and alerts to guide continuous improvements in models, prompts, and pipelines.
• Apply systems thinking to design and optimize AI and LLM systems, balancing quality, scalability, latency, cost, and operational complexity, while implementing efficiency improvements using model selection, prompt design, batching, caching, and retrieval strategies.
• Define and implement evaluation and observability frameworks for AI systems, including automated testing, task-specific benchmarks, regression testing for prompts, human-in-the-loop validation, and performance monitoring.
• Build and integrate AI models into backend systems and APIs to support both real-time and batch inference, ensuring solutions are production-ready, scalable, and efficient.
• Apply NLP and ML techniques to tasks such as information extraction, semantic search and retrieval, text classification, summarization, and reasoning over text and documents.
• Collaborate closely with engineering and infrastructure teams to deploy solutions using containerized and cloud-based environments (e.g., GitHub, Docker, AWS), applying modern deployment and infrastructure practices.
• Collaborate with product managers, business stakeholders, and domain experts to translate complex, ambiguous business problems into actionable technical solutions, and communicate progress, trade-offs, and outcomes to relevant stakeholders.
• Continuously learn and adapt to advancements in NLP and Generative AI to ensure solutions remain innovative and effective.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience).
• 3+ years of experience as a Data Scientist, Machine Learning Engineer, or applied AI practitioner, with a strong foundation in computer science, algorithms, and software development. 
• Advanced programming skills in Python, with experience building production-grade systems beyond research or experimentation.
• Solid understanding of machine learning and applied AI concepts, with experience taking solutions from prototype to production.
• Hands-on experience designing, building, and deploying LLM-driven or GenAI applications, including familiarity with vector databases, embeddings pipelines, or semantic search systems.
• Practical experience with cloud-based deployments and infrastructure tools (e.g., AWS, Docker, GitHub) and an understanding of modern DevOps practices, containerization, orchestration, caching strategies, and cost-aware design.
• Strong problem-solving skills and systems thinking, with the ability to balance trade-offs across model quality, scalability, inference latency, cost, and operational complexity.
• Ability to interpret and implement research ideas and algorithms, actively contributing to research and development initiatives while translating them into production solutions.
• Excellent communication and collaboration skills, with experience working closely with product managers, engineers, and domain experts to deliver actionable technical solutions.
• Passion for learning and staying current with the rapidly evolving AI/ML landscape, including emerging best practices for GenAI applications.
• Strong ownership and initiative, with the ability to independently drive projects from problem definition to delivery, while being a team player and contributing to the overall success of the data science team.

At Octus, we consider a range of factors in connection with compensation decisions, including experience, skills, location, and our business needs and limitations. As a result, compensation may vary within and across similar roles and positions. Please note that the salary range information below is a good faith estimate for this position and actual compensation for any individual may fall outside this range if warranted by the circumstances applicable to that individual. If we identify a role that would be suitable for a broader range of skills and experience such that we would consider hiring at multiple levels then the range listed below may reflect that breadth.

The salary range estimate for this position is $130,000-$145,000.

The actual compensation will be at Octus’ sole discretion and will be determined by the aforementioned and other relevant factors. This position is eligible for a performance-based annual bonus.

Please note: This position is not eligible for immigration sponsorship. 

Company Overview

We are the better way to work in finance. As private equity’s value creation partner, we sit at the heart of PE—where sponsors and CFOs meet. Through financial consulting rooted in data, technology, and AI, we help clients drive value—supporting the office of the CFO to drive end-to-end value creation.

If you crave challenging work and are looking to grow, come solve complex issues alongside 1,600+ finance & technology experts in a supportive, collaborative environment.

Backed by premier private equity firms and headquartered in New York with 11 offices around the globe, we are a high-growth, entrepreneurial firm looking for people who want to be part of building something great. Come make your mark. 

AI Governance Team, Responsible AI/ML Data Scientist

We are seeking a Vice President, Responsible AI/ML Data Scientist on our AI Governance Team who combines technical expertise in Data Science and Machine Learning with a strong advisory lens to help drive trustworthy AI solution development across Accordion's practices. In this role, you will serve as an internal subject matter expert, embedding responsible AI principles and controls across the AI development lifecycle to drive validated, explainable, compliant, and auditable AI solutions. Drawing on your experience with AI policy, regulatory standards, and testing methodologies, you will translate governance requirements into actionable and testable playbooks that teams across the business can apply consistently. This position is critical to co-designing AI solutions with embedded Responsible AI practices aligned to Accordion's AI Governance principles. As Accordion scales AI capabilities across its practice areas, this role will provide the validation rigor and oversight needed to ensure we develop AI solutions our clients and firm can trust.

This position must be based in our New York City, Chicago, or San Francisco office and is a hybrid role with the flexibility to work remotely 2 days a week.

This position is not eligible for immigration sponsorship.

What You’ll Do:

Governance & Technical Standards

• Translate AI governance, regulatory, and compliance requirements into testable operational Responsible AI playbooks with quantifiable standards for transparency, accuracy, fairness, privacy, and accountability — tailored to Accordion's AI solutions and business model
• Develop frameworks and guidance supporting systematic evaluation, testing, and risk mitigation that enable auditability, lineage, and transparent decision records
• Build evaluation frameworks that guide data scientists and engineering teams in developing, testing, and monitoring AI systems requirements; define mitigation strategies, detection methodologies, and acceptability thresholds
• Develop and implement testing principles for bias detection, model robustness evaluation, privacy preservation, and AI/privacy regulation alignment across different AI architectures and data types
• Establish benchmarks and governance processes aligned with industry standards (e.g., NIST AI RMF, EU AI Act, ISO 42001) that enable auditable AI transparency and explainability against applicable regulations and Accordion standards

Testing & Validation

• Collaborate with data science and engineering teams across our AI solution practices to embed responsible AI controls throughout the development lifecycle
• Develop repeatable testing and validation playbooks and evaluation frameworks for use across practice areas
• Pressure test AI solutions for accuracy, reliability, and trustworthiness, including output anomaly detection, logging, and observability mechanisms
• Develop methods to produce model cards and support audit trails for outputs across Accordion's business practice pillars and Client solutions

Cross-Functional Advisory

• Act as the firm's internal subject matter expert on Responsible AI topics including algorithmic fairness, transparency, privacy preservation, safety protocols, and risk mitigation strategies across the AI lifecycle
• Partner with Legal/Privacy/Risk, Technology, and D&A teams to translate regulatory requirements into actionable and measurable controls and governance structures
• Collaborate with teams developing AI solutions on risk-informed design decisions — advising on model selection, testing approaches, and appropriate guardrails
• Establish Responsible AI frameworks and operational playbooks that demonstrate alignment with evolving AI regulations (e.g., EU AI Act, GDPR, CCPA) and sector-specific requirements
• Monitor emerging AI regulations, industry standards, and responsible AI methodologies — translating insights into actionable internal design guidance
• Where applicable, support higher-risk or higher-profile engagements and AI product development where rigorous testing and evaluation of AI solutions is required

You Have:

• 5+ years of hands-on experience in data and ML, including developing, deploying, and evaluating solutions using a range of skillsets (e.g., statistics, machine learning, NLP, data visualization)
• Bachelor's degree in Computer Science, Data Science, Information Systems, Mathematics, Engineering, Statistics, or a related quantitative field
• Proven technical experience in regulated or complex industries with demonstrated collaboration across Legal/Risk/Compliance, Security, and AI/ML engineering teams
• Track record of operationalizing responsible AI through technical development frameworks, testing protocols, and quantitative evaluation that embed fairness, transparency, and accountability into production AI systems
• Strong programming proficiency in Python, R, or similar languages, with experience in ML frameworks and responsible AI tooling
• Understanding of AI and privacy regulations and standards — including EU AI Act, NIST AI RMF, GDPR, CCPA, and ISO 42001 — and the ability to translate regulatory mandates into technical controls
• Exceptional communication skills with the ability to convey complex technical and regulatory considerations to non-technical stakeholders, including leadership and cross-functional teams

Preferred Qualifications:

• Advanced degree (Master's) in Computer Science, Data Science, Statistics, Information Systems, Engineering, or a related quantitative discipline
• Consulting experience at a leading management or professional services firm
• Direct experience working with Legal/Privacy/Compliance teams on AI, data governance, or emerging technology matters
• Familiarity with responsible AI tooling, red teaming platforms, or model evaluation frameworks

You Are:

• Energized by ensuring AI systems that solve complex business challenges are built responsibly — with fairness, accuracy, transparency, and privacy by design embedded from the start
• A cross-functional collaborator who navigates matrixed organizational dynamics, working seamlessly with Legal/Risk/Compliance, Technology/Engineering, and business teams to establish AI principles, governance playbooks, and technical roadmaps
• A clear communicator who can translate AI governance concepts and technical risk findings into language that resonates with non-technical stakeholders and leadership
• A proactive problem-solver who thrives on staying ahead of AI governance expectations and emerging regulations — and can turn frameworks into practical controls
• An adaptive contributor who embraces the pace of consulting, working across diverse sectors and practice areas

The annual salary for this role ranges from: $160,000 to $175,000 + benefits. This role is also eligible for bonuses, based on individual and company performance. Actual compensation packages are determined by evaluating a wide array of factors unique to each candidate, including but not limited to, geographic location, skill set, years and depth of experience, education, certifications, cost of labor and internal equity. 

Accordion is an Equal Opportunity Employer. We are committed to building a team that represents a variety of backgrounds, perspectives, and skills.  We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical​​​ condition, pregnancy, genetic information, gender, sexual orientation, gender identity or ​expression, veteran status, or any other status protected under federal, state, or local law.

Please note that as per Accordion policy, we do not accept unsolicited resumes from third-party recruiters unless such recruiters are engaged to provide candidates for a specified opening and in alignment with our inclusive diversity values. Any employment agency, person or entity that submits an unsolicited resume does so with the understanding that Accordion will have the right to hire that applicant at its discretion without any fee owed to the submitting employment agency, person or entity.

#LI-HA1

Please note: This position is not eligible for immigration sponsorship. 

Company Overview 

We are the better way to work in finance. As private equity’s value creation partner, we sit at the heart of PE—where sponsors and CFOs meet. Through financial consulting rooted in data, technology, and AI, we help clients drive value—supporting the office of the CFO to drive end-to-end value creation. 

If you crave challenging work and are looking to grow, come solve complex issues alongside 1,600+ finance & technology experts in a supportive, collaborative environment.

Backed by premier private equity firms and headquartered in New York with 11 offices around the globe, we are a high-growth, entrepreneurial firm looking for people who want to be part of building something great. Come make your mark.  

AI Governance Team, AI Risk and Operations 

We are seeking a Vice President, AI Risk and Operations on our AI Governance Team who will play a critical role in maturing and scaling Accordion's enterprise-wide AI Governance risk and operations program. In this role, you will serve as an internal governance practitioner and cross-functional advisor, embedding AI governance policies, procedures, and controls across our operating model for internal AI solutions — with the expectation that this scope will evolve to include client-facing engagements over time. Drawing on your experience with AI policy, frameworks, and risk program design, you will translate complex governance requirements into actionable, scalable implementation systems. This position requires seamlessly bridging the gap between cutting-edge AI development and responsible governance, partnering with senior stakeholders across Practices, Technology, Legal, and Emerging Technology to enabling trusted use and development of AI.  

This position can be based in our New York City or Chicago offices and is a hybrid role with the flexibility to work remotely 2 days a week. Ideal candidates should be local to the desired location. 

This position is not eligible for immigration sponsorship. 

What You’ll Do 

Governance & Frameworks 

• Enhance the enterprise AI Risk Management Framework, including AI use case and supply chain inventory management, use case risk classification taxonomies, and risk-tiering mechanisms aligned to internal standards and emerging regulatory expectations 
• Design and operationalize scalable, end-to-end governance processes across the AI lifecycle  
• Build and maintain AI governance playbooks, risk appetite frameworks, and responsible AI standards  
• Identify opportunities to strengthen controls and oversight through process automation, tooling improvements, and governance workflow optimization 

Cross-Functional Collaboration 

• Partner with Legal, Technology, and Consulting Practice Groups to embed explainability, bias mitigation, and ethical AI principles directly into use case development and delivery workflows 
• Manage the firm's AI Governance obligations and processes end-to-end, including supporting AI and related vendor management supply chain   
• Coordinate with Legal teams to ensure AI systems meet applicable jurisdictional and regulatory requirements, including proactive engagement on evolving global AI legislation 
• Facilitate stakeholder working groups, cross-functional governance forums, and executive communications on AI risk priorities, testing outcomes, and documentation requirements 
• Maintain the internal AI risk register and escalate issues through appropriate governance channels 
• Liaise with Technology and Practices to ensure data lineage and quality standards are met for AI solutions 

Monitoring & Oversight 

• Establish, track, and report on key risk indicators (KRIs) for AI systems in partnership with the Responsible AI Data Scientist and AI Governance Lead, across internal and client-facing AI solutions and products 
• Monitor alignment with global AI regulatory frameworks (e.g., EU AI Act, NIST AI RMF) and support responses to regulatory inquiries, audits, or reviews 
• Evaluate risks associated with third-party and vendor AI solutions across the full sourcing lifecycle — including procurement, onboarding, integration, and ongoing performance oversight — and identify operational guardrails and controls 
• Assess design and operating effectiveness of AI controls against internal policy and external standards, especially for AI, privacy, and industry regulations, under Legal's guidance 
• Produce high-quality audit findings reports with clear recommendations for remediation and monitor implementation of agreed action plans 
• Stay current on AI regulation, industry standards, and emerging best practices 

Internal Enablement (Evolving) 

• Support the development of AI Governance updates, training/awareness programs, and culture-building initiatives to embed responsible AI practices across the organization 
• Contribute to Accordion's Trustworthy AI methodology and internal IP development 
• Over time, support client-facing AI governance assessments, risk remediation roadmaps, and responsible AI frameworks as the program matures and scope expands  

You Have 

• Bachelors degree in Finance, Business Administration, or related field  
• 7+ years’ experience in Risk Operations, Privacy Programs, or PMO/IMO, with at least 3 years directly focused on AI governance or oversight - preferably within professional services, consulting, or PE-backed environments. 
• Demonstrated experience leading complex, multi-jurisdictional integrations and/or global expansion programs, with a track record of building and running structured governance. 
• Strong knowledge of AI and privacy regulations and standards — including EU AI Act, NIST AI RMF, GDPR, CCPA, and ISO 42001 — and the ability to translate regulatory mandates into operational controls 
• Strong grasp of AI/ML technical risks (e.g., bias, model drift, robustness) and associated controls 
• Strong familiarity with regulatory and operational considerations relevant to cross-border professional services (e.g., data/privacy, employment, tax, licensing), and the ability to operationalize them into business processes. 
• Exceptional communication skills with the ability to convey complex technical and regulatory considerations to non-technical stakeholders, including leadership and cross-functional teams 
• Comfortable operating in a high-growth, entrepreneurial environment, with a bias toward structure, scalability, and execution discipline. 
• Strong analytical and problem-solving skills  
• AI usage and familiarity required 
• Excellent attention to detail and organizational skills   
• Ability to adapt to changing priorities and work effectively in a dynamic environment   

Preferred Qualifications 

• Experience in Data Risk Management or direct collaboration with AI/ML development teams 
• Familiarity with AI risk management platforms or tools for model monitoring, documentation, and compliance reporting 
• Experience designing training, awareness, or enablement programs focused on AI risk, model governance, or responsible AI practices 
• Certifications in risk, privacy, or compliance (e.g., CIPP, CIPM, CRISC, CRCM) 
• Passion for AI and a desire to build a world-class risk management function  

You Are 

• Energized by building and scaling governance programs that make AI deployment safer, more trustworthy, and more compliant — across a dynamic, fast-moving consulting environment 
• A cross-functional collaborator who navigates matrixed organizational dynamics, working seamlessly with Legal/Risk/Compliance, Technology/Engineering, and business teams — and can drive decision making and ownership across diverse stakeholders 
• A clear communicator who can translate AI governance concepts, regulatory requirements, and risk findings into actionable guidance for business, technical, and executive audiences 
• A proactive problem-solver who thrives on staying ahead of AI governance expectations and emerging regulations, and can turn frameworks into operational reality 
• An adaptive contributor who embraces the breadth and pace of consulting, working across diverse sectors and practice areas 

The annual salary for this role ranges from: $130,500 to $145,000 + benefits. This role is also eligible for bonuses, based on individual and company performance. Actual compensation packages are determined by evaluating a wide array of factors unique to each candidate, including but not limited to, geographic location, skill set, years and depth of experience, education, certifications, cost of labor and internal equity.  

Accordion is an Equal Opportunity Employer. We are committed to building a team that represents a variety of backgrounds, perspectives, and skills.  We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical​​​ condition, pregnancy, genetic information, gender, sexual orientation, gender identity or ​expression, veteran status, or any other status protected under federal, state, or local law. 

Please note that as per Accordion policy, we do not accept unsolicited resumes from third-party recruiters unless such recruiters are engaged to provide candidates for a specified opening and in alignment with our inclusive diversity values. Any employment agency, person or entity that submits an unsolicited resume does so with the understanding that Accordion will have the right to hire that applicant at its discretion without any fee owed to the submitting employment agency, person or entity. 

#LI-HA1

The Team

Our AI research team sits at the heart of our mission to unlock new dimensions of biological understanding. You will leverage state-of-the-art AI to accelerate discovery and drive transformative insights in biology—developing novel AI models purpose-built for biological research, engineering robust systems that enable breakthrough science at unprecedented scale, and translating these advances into practical tools that empower researchers worldwide.

Our approach is comprehensive and integrated, bringing together world-class AI model development, exceptional engineering talent, high-quality biological sciences and data, powerful computing infrastructure, and strategic partnerships. Success requires excellence across five interconnected pillars: training frontier AI models specifically for biology; building engineering systems that maximize research velocity and efficiency; executing a sophisticated biological and data strategy that fuels AI development; operating a world-class AI compute platform; and creating impactful products that transform AI capabilities into accessible scientific tools.

The Opportunity

This is an opportunity to shape the future of biological research by pushing the boundaries of what AI can achieve in science. You’ll work alongside leading experts in AI and biology, with the resources and mandate to tackle some of the most important questions in human health — advancing frontier AI research, accelerating engineering velocity, connecting rich biological data to AI systems, enabling reliable compute across environments, and translating models and data into usable, scalable applications that drive scientific impact.

The role is part of the Data team, which is responsible for maximizing the speed, agility, and capability of biological AI research by connecting public data resources and Biohub's experimental platforms to AI systems. The data that trains biological frontier models comes in dozens of modalities (sequences, images, spatial coordinates, time series, molecular structures, metadata, publication artifacts, …) each with its own noise characteristics, biases, and information content. The question of how AI can reason across these diverse descriptions of biology to answer specific experimental questions is one of the core challenges in biology.

You will define the data approach to train our reasoning system. To do so, you will operate with broad scope and high autonomy, influencing roadmap decisions across teams while mentoring individual contributors. Success in this role means scaling data systems that are not only large, but adaptive, interpretable, and scientifically grounded, accelerating progress toward robust biological frontier models and ultimately advancing human health. We're looking for data scientists who can work at this frontier: people who understand scientific experimentation deeply, think creatively about data representations and tokenization strategies, and have experience building reasoning systems. You'll work directly with experimental and computational scientists, data scientists and AI researchers to define what the models see and how they see it, and data engineers to make this work at scale. This is a role for someone who wants to invent the methods that make biological frontier models possible.

What You'll Do

• Design reasoning tasks for our models.
• Build training datasets that capture biology experiments, including experimental design, hypothesis generation, evidence interpretation, and scientific inference
• Design training strategies that teach reasoning capabilities, working closely with AI Research to translate data approaches into model behavior
• Create evaluation and benchmarking frameworks that measure reasoning quality and analyze model behavior to influence the next set of evaluations, environments, and data.
• Partner with Scientific Data Strategy and Data Engineering to identify and acquire source materials (literature, protocols, experimental records) that contain reasoning signal
• Set technical direction for reasoning data efforts, influencing priorities and mentoring other data scientists working in this area

What You'll Bring

• PhD in machine learning, computational biology, or another quantitative field
• Hands on understanding of how scientists reason across diverse experimental systems as obtained from hands-on experience with laboratory science in biology, biochemistry, or chemistry
• Experience curating or creating training data and tokenization strategies for reasoning models (RLVR, synthetic reasoning traces, RL with Tool Use)
• Track record of novel methodological contributions (publications, open-source tools, or production systems)
• Familiarity with evaluation methodology for complex, open-ended tasks where ground truth is ambiguous
• Strong computational skills (Python, data processing at scale); ability to work with large text corpora and structured data
• Creative, first-principles thinking about how to structure data for learning

Compensation

The Redwood City, CA & New York City, NY base pay range for a new hire in this role is $214,000 - $268,000. New hires are typically hired into the lower portion of the range, enabling employee growth in the range over time. Actual placement in range is based on job-related skills and experience, as evaluated throughout the interview process. 

This position may be eligible to participate in Biohub's discretionary annual performance bonus program. Bonus eligibility and targets are determined in accordance with Biohub's total rewards philosophy and may vary by role.

Better Together

As we grow, we’re excited to strengthen in-person connections and cultivate a collaborative, team-oriented environment. This role is a hybrid position requiring you to be onsite for at least 60% of the working month, approximately 3 days a week, with specific in-office days determined by the team’s manager. The exact schedule will be at the hiring manager's discretion and communicated during the interview process.

Benefits for the Whole You 

We’re thankful to have an incredible team behind our work. To honor their commitment, we offer a wide range of benefits to support the people who make all we do possible. 

• Provides a generous employer match on employee 401(k) contributions to support planning for the future.
• Paid time off to volunteer at an organization of your choice. 
• Funding for select family-forming benefits. 
• Relocation support for employees who need assistance moving

If you’re interested in a role but your previous experience doesn’t perfectly align with each qualification in the job description, we still encourage you to apply as you may be the perfect fit for this or another role.

#LI-Hybrid 

Biohub operates one of the largest AI compute clusters dedicated to biology, spanning three frontier research institutes with some of the world's leading biologists. We're not a startup trying to find product-market fit, and we're not a pharma company optimizing a pipeline. We're building frontier AI for fundamental science, as open science, at a scale no one else is doing. This is a unique moment for scientific acceleration. The problems are among the hardest and most impactful problems you can choose to work on, and we move at a pace that meets this moment. 

Our research spans:

• Frontier molecular modeling, from protein language models (e.g., ESM) to structure prediction (e.g., ESMFold) and beyond. 
• Scaled biological foundation models trained on some of the largest GPU clusters dedicated to science
• Imaging foundation models trained across the world's largest microscopy datasets
• Reasoning and agentic systems that connect frontier LLMs with biological foundation models
• Mechanistic interpretability of biological foundation models: extracting new biological knowledge directly from model weights
• Scientific data at unprecedented scale: AI systems to collect, curate, and learn from some of the richest biological datasets ever assembled

Join Our Team!

As a Research Scientist, you'll build the models and systems that define what AI can do in biology: foundation models, reasoning, reinforcement learning, and multi-agent systems at frontier scale.

What You'll Do

• Build on and advance the AI systems at the frontier of biology to accelerate science
• Design novel model architectures and scale pre-training pipelines for our biology foundation models.
• Build post-training systems that drive biological discovery: RL, reward modeling, reasoning, and multi-agent orchestration for long-horizon scientific tasks.
• Design evaluation frameworks and AI systems with scientists across Biohub and beyond, grounded in real biological outcomes and real world impact.
• Engage the wider scientific community through impactful publications, open-source releases, and collaborations worldwide.

What You'll Bring

• PhD in computer science or a related field, or equivalent experience
• Significant experience building and scaling deep learning systems, ideally in research-driven environments
• Strong engineering and scientific fundamentals
• Comfort with ambiguity, rapid iteration, and loosely-defined research problems
• Strong communication skills across technical and scientific audiences

Compensation

The future anticipated Redwood City, CA, and New York City, NY base pay range for a role in this field is $214,000 to $375,000 annually. Final compensation is based on the level at which you are hired. Actual placement in range is based on job-related skills and experience, as evaluated throughout the interview process.

Benefits for the Whole You 

We’re thankful to have an incredible team behind our work. To honor their commitment, we offer a wide range of benefits to support the people who make all we do possible. 

• Provides a generous employer match on employee 401(k) contributions to support planning for the future.
• Paid time off to volunteer at an organization of your choice. 
• Funding for select family-forming benefits. 
• Relocation support for employees who need assistance moving

Please note that applying to this opportunity does not guarantee that we will be in touch with you regarding our opportunities. Our recruiting team will contact you if your experience aligns with the skills we seek for future open positions. We will keep your interest on file, contact you as opportunities arise, and send you information about the exciting work we are doing at Biohub. You can opt out at any time! 

#LI-Hybrid 

About the team + role

We are building an elite team, applying frontier technologies to the world’s biggest financial problems. We’re looking for bold thinkers. Sharp problem-solvers. Builders who are wired to make an impact. Robinhood isn’t a place for complacency, it’s where ambitious people do the best work of their careers. We’re a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards.

The Fraud Data Science team safeguards Robinhood and its customers by detecting and preventing fraud and abuse across our platform. We leverage machine learning and analytics to combat malicious behavior in real time, supporting a safe and trusted experience for all users. Our work has a direct impact on customer security, company risk posture, and regulatory compliance.

As a Senior Data Scientist on the Fraud team, you will own the design and deployment of ML solutions that proactively surface suspicious activity, reduce financial loss, and improve fraud detection precision. You’ll collaborate closely with engineering, product, risk, and compliance partners to influence system architecture, shape policy through data, and enhance the safety and integrity of our platform.

This role is based in our Menlo Park office, with in-person attendance expected at least 3 days per week.

At Robinhood, we believe in the power of in-person work to accelerate progress, spark innovation, and strengthen community. Our office experience is intentional, energizing, and designed to fully support high-performing teams. 

What you’ll do

• Design and deploy fraud detection models to protect Robinhood users and assets in real time
• Analyze behavioral data to uncover emerging fraud vectors and support rapid incident response
• Develop robust data pipelines and monitoring systems to ensure model accuracy and reliability
• Partner with engineering and product teams to implement safeguards and user-facing features
• Guide experimentation strategy and contribute to long-term fraud prevention roadmap

What you bring

• 5+ years of experience in data science or applied ML, with a focus on fraud detection or risk mitigation
• Advanced proficiency in Python and SQL; experience with ML frameworks like XGBoost, LightGBM, or TensorFlow
• Strong statistical acumen with experience in anomaly detection, pattern recognition, and A/B testing
• Excellent communication skills and ability to influence decision-making across technical and non-technical audiences
• A collaborative mindset and proactive approach to navigating ambiguity in fast-paced environments

What we offer

• Challenging, high-impact work to grow your career.
• Performance-driven compensation with multipliers for outsized impact, bonus programs, equity ownership, and 401(k) matching.
• Best-in-class benefits to fuel your work, including 100% paid health insurance for employees with 90% coverage for dependents.
• Lifestyle wallet — a highly flexible benefits spending account for wellness, learning, and more.
• Employer-paid life & disability insurance, fertility benefits, and mental health benefits.
• Time off to recharge including company holidays, paid time off, sick time, parental leave, and more!
• Exceptional office experience with catered meals, events, and comfortable workspaces.

Small business owners trust Gusto with something fundamental: making sure their people get paid, on time, every time. As a Principal Data Scientist on the Payments team, you'll sit at the intersection of financial infrastructure and data science — translating the complexity of a high-scale payments ecosystem into the insights and models that shape how we build, operate, and improve our payments products. 

In this role you will work closely with our Product, Engineering, Design, Finance, and other Data teams to become an expert in the data for your domain, define and track metrics that help us understand our business performance, and dive deep into our payments data to deliver insights and answer questions. You’ll also integrate AI-assisted practices to accelerate analysis, enhance rigor, and expand the reach of insights across Gusto. 

This is a role for someone who's equally comfortable diving into ledger-level transaction data and standing in front of a payments engineering team to influence architecture decisions. If you want your work to directly protect and improve the financial lives of hundreds of thousands of small businesses, this is the role.

Here’s what you’ll do day-to-day:

• Own payments intelligence end-to-end: Design and maintain measurement frameworks for payment success rates, failure root causes, retry strategies, and settlement timing — becoming the authoritative source of truth for payments health across the org.
• Partner with payments engineering: Embed with backend engineering teams to understand infrastructure constraints and data availability, translate that knowledge into well-scoped experiments, and influence roadmap prioritization with data-backed recommendations.
• Drive experimentation at scale: Design and analyze A/B tests and quasi-experiments across payment flows — including retry logic, routing decisions, and failure recovery — ensuring statistical rigor even in low-conversion, high-variance payment environments.
• Build predictive models: Develop and deploy models for payment failure prediction, risk scoring, and anomaly detection that operate at Gusto's transaction volume, partnering with Machine Learning Engineers and Product Engineers on productionization and monitoring.
• Build AI-native data products: Go beyond using AI to go faster; build data products and automated workflows with AI as a core component. Know what "good" looks like for a clustering model or automated workflow, when to trust the output, when to challenge it, and how to build guardrails that let the team scale AI use responsibly in a regulated financial context.
• Own the business narrative: Be the person payments product and engineering leadership calls before an exec review, not after. Comfortable defending methodology on ARR projections, CX attribution, and cost savings in a room with skeptics. Translate nuanced statistical results and model outputs into clear, actionable narratives for payments engineers, product managers, finance partners, and executive leadership.

Here’s what we're looking for:

• 10+ years of experience in Data Science or a closely related quantitative role at a product-focused software company, with meaningful exposure to payments, fintech, or financial infrastructure.
• Deep payments domain knowledge: Hands-on experience with payments infrastructure: ACH, card networks, bank integrations, settlement flows, or equivalent, with a strong intuition for how backend systems shape data availability and quality.
• Strong SQL and Python skills, with experience querying large-scale transactional datasets and building reproducible analytical pipelines.
• Proven experimentation and causal inference expertise: Ability to design rigorous experiments in challenging payment contexts (rare events, high variance, non-stationarity) and communicate trade-offs clearly.
• AI-native mindset: Has built data products and analytical workflows with AI as a core component, not just used AI to go faster. Knows what "good" looks like for a clustering model or automated workflow, when to trust the output, when to challenge it, and how to build guardrails that scale AI use responsibly in a financial context.
• Cross-functional influence: Track record of collaborating with and influencing backend engineers and technical stakeholders, not just product and business partners.

Our cash compensation amount for this role is targeted at $184,000/yr - $210,000/yr in Denver, and $217,000/yr - $250,000 for San Francisco and New York. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed above.

About the role:

We are seeking an exceptional Staff Data Scientist to join our Garner Health Intelligence team to help build and evolve our provider data insights. You’ll work with our clinical researchers, data scientists, and data engineers to improve the rules and models we use to evaluate provider performance. Your work will directly impact our member recommendations and provider-facing data products.

You will also be a technical leader, raising the bar for how we conduct analysis, build models, and deploy data science code by improving standards and mentoring team members.

Where you will work:

This role will be based in our New York City office. You must be willing to work in the office 3 days per week on Tuesday, Wednesday and Thursday. 

What you will do:

• Partner with product managers and clinical experts to lead projects shaping how we define and measure provider cost and quality
• Drive the evolution and construction of tools we use to write, assess, and combine rules measuring provider behaviors
• Build and maintain data-driven algorithms, including AI and machine learning models, to improve our provider recommendations estimates
• Serve as a subject matter expert for statistical modeling, improving methods for evaluating current models and future changes
• Partner with data engineers to design scalable data pipelines that ingest, clean, and enrich healthcare provider data
• Collaborate cross-functionally with product, engineering, research, and operations teams to translate business needs into data-driven solutions
• Define data science standards and best practices, and mentor team members to raise the technical capabilities of the data science team

The ideal candidate has:

• 8+ years professional experience in data science, product analytics, and/or healthcare
• A demonstrated ability to independently solve difficult problems and deliver high quality results to senior stakeholders
• Strong Python and SQL skills
• Expertise in statistical modeling techniques and a track record of building impactful predictive models
• A willingness to “roll up your sleeves” and do whatever is necessary to ensure company success
• Experience working in a rapidly evolving startup environment.
• A desire to be a part of our mission to improve the U.S. healthcare system
• A desire to be a part of a high-performing, mission-driven team that operates with intense urgency, a strong sense of individual accountability, and a commitment to authentic feedback

Technologies we use: 

• Python and Jupyter Notebooks, SQL (Snowflake), Argo, DBT, AWS, Terraform

This is a unique opportunity to join a fast-growing company in a transformative role, helping shape the future of healthcare.

Please note: we are unable to sponsor or take over sponsorship of an employment visa at this time.

Compensation Transparency:

The target salary range for this position is $263,000 - $300,000. Individual compensation for this role will depend on various factors, including qualifications, skills, and applicable laws. In addition to base compensation, this role is eligible to participate in our equity incentive and competitive benefits plans, including but not limited to: flexible PTO, Medical/Dental/Vision plan options, 401(k), Teladoc Health and more.

About the role:

We are seeking an exceptional Applied Scientist to work on core search systems that power our product, with a particular focus on provider selection and recommendation algorithms. You will play a pivotal role in shaping how Garner selects high-value providers for members by developing the evaluation frameworks, objective functions, and algorithms that drive our search experience.

This role is ideal for someone who is excited by ambiguous, high-impact problems and can translate messy real-world constraints into robust algorithmic systems. You will partner closely with Product and Engineering to design and deliver production algorithms that balance provider quality, affordability, and access.

Where you will work:

This role will be based in our New York City office. You must be willing to work in the office 3 days per week on Tuesday, Wednesday and Thursday. 

What you will do:

• Build and own the algorithms that power Garner’s search
• Design objective functions, scoring frameworks, and decision logic that balance competing goals such as quality, cost, and access
• Translate ambiguous product and business problems into rigorous algorithmic approaches that can be deployed in production
• Partner with engineers to productionize systems at scale
• Identify where applied machine learning, optimization, heuristics, or rules-based methods are the right tool for a problem
• Develop a strong understanding of the healthcare economy

The ideal candidate has:

• 4+ years of experience using Python and SQL to build production, data-driven algorithms
• Strong applied problem-solving skills, with the ability to define good metrics and design algorithms under ambiguity
• Strong judgment in choosing between statistical models, heuristics, optimization approaches, and simpler algorithmic methods depending on the problem
• Strong communication skills and the ability to synthesize complex algorithmic ideas for business and technical stakeholders
• A desire to be a part of a high-performing, mission-driven team that operates with urgency, a strong sense of individual accountability, and a commitment to authentic feedback

Technologies we use: 

• Python, SQL, AWS, Snowflake, pandas, XGBoost

This is a unique opportunity to join a fast-growing company in a transformative role, helping shape the future of healthcare.

Please note: we are unable to sponsor or take over sponsorship of an employment visa at this time.

Compensation Transparency:

The target salary range for this position is $200,000 - $240,000. Individual compensation for this role will depend on various factors, including qualifications, skills, and applicable laws. In addition to base compensation, this role is eligible to participate in our equity incentive and competitive benefits plans, including but not limited to: flexible PTO, Medical/Dental/Vision plan options, 401(k) with company match, flexible spending accounts, Teladoc Health and more.

About the role:

We are seeking an exceptional Data Scientist II to join our Central Data team. This role will report to the Director, Data Science & Analytics. This role is critical to shaping Garner’s Product & Growth strategy - from evaluating the impact of our products to building models that drive engagement. You will collaborate with data, product & engineering to improve the effectiveness of Garner’s member facing product – e.g., answering questions like, “what features of our product drive engagement?”, “What is the best time of day and channel to outreach to members?”, or “What data concepts do we need to build out to improve our understanding of what drives someone to engage with Garner?” Your work will directly impact our member-facing products and inform strategic product decisions.

Where you will work:

This role will be based in our New York City office. You must be willing to work in the office 3 days per week on Tuesday, Wednesday and Thursday. 

What you will do:

• Identify and measure success of product efforts through goal setting, AB testing, forecasting, and monitoring of key product metrics to understand trends
• Own projects end to end including problem definition, implementation, validation, & iteration 
• Research, develop, and maintain data pipelines, statistical models, and advanced analytical solutions, including AI & machine learning models 
• Collaborate cross-functionally with data scientists, product leaders, engineering & operations teams  to shape how we define product & member engagement metrics, apply statistical rules, and model member behavior
• Synthesize & present your work to Garner leadership to drive company level decisions
• Contribute to improving data science practices, tooling, and documentation

What you will bring to the team:

• 2+ years of experience in data science, applied analytics, or a related quantitative field (industry, academia, or both)
• 2+ years of experience using SQL and Python and/or R to query, analyze, and manipulate data
• 2+ years of experience building data models, using more advanced analytics methods, statistical modeling, and/or data processing
• Excellent verbal and written communication skills, with ability to present information and analysis results effectively
• A demonstrated ability to independently solve difficult problems and deliver high quality results to senior stakeholders
• A willingness to “roll up your sleeves” and do whatever is necessary to ensure company success, ideally in a startup environment
• A desire to be a part of a high-performing, mission-driven team that operates with intense urgency, a strong sense of individual accountability, and a commitment to authentic feedback

This is a unique opportunity to join a fast-growing company in a transformative role, helping shape the future of healthcare.

Please note: we are unable to sponsor or take over sponsorship of an employment visa at this time.

Compensation Transparency:

The target salary range for this position is $158,000 - $175,000. Individual compensation for this role will depend on various factors, including qualifications, skills, and applicable laws. In addition to base compensation, this role is eligible to participate in our equity incentive and competitive benefits plans, including but not limited to: flexible PTO, Medical/Dental/Vision plan options, 401(k), Teladoc Health and more.

About the role:

We are seeking an exceptional Data Scientist to join our Garner Health Intelligence team to help build and evolve our provider data insights. You’ll work with our clinical researchers, data scientists, and data engineers to improve the rules and models we use to evaluate provider performance – e.g., answering questions like, how do we know which providers deliver better surgical outcomes or what data concepts do we need to build out to improve our analysis. Your work will directly impact our member recommendations and provider-facing data products.

Where you will work:

This role will be based in our New York City office. You must be willing to work in the office 3 days per week on Tuesday, Wednesday and Thursday. 

What you will do:

• Partner with data scientists and clinical experts to shape how we define provider performance metrics, apply statistical rules, and model provider behavior
• Drive the evolution and construction of tools we use to write, assess, and combine rules measuring provider behaviors
• Build and maintain data-driven algorithms, including AI and machine learning models, to improve our provider recommendations estimates.
• Design scalable data pipelines that ingest, clean, and enrich healthcare provider data.
• Collaborate cross-functionally with product, engineering, research, and operations teams to translate business needs into data-driven solutions.

The ideal candidate has:

• 2-5 years professional experience in data science, product analytics, and/or healthcare
• A demonstrated ability to independently solve difficult problems and deliver high quality results to senior stakeholders
• Strong Python and SQL skills
• A willingness to “roll up your sleeves” and do whatever is necessary to ensure company success
• Experience working in a rapidly evolving startup environment
• A desire to be a part of our mission to improve the U.S. healthcare system
• A desire to be a part of a high-performing, mission-driven team that operates with intense urgency, a strong sense of individual accountability, and a commitment to authentic feedback

Technologies we use: 

• Python, TypeScript, React, NodeJS, Kubernetes, Istio, Postgres, ElasticSearch, NATS, AWS, Terraform

This is a unique opportunity to join a fast-growing company in a transformative role, helping shape the future of healthcare.

Please note: we are unable to sponsor or take over sponsorship of an employment visa at this time.

Compensation Transparency:

The target salary range for this position is $160,000 - $185,000. Individual compensation for this role will depend on various factors, including qualifications, skills, and applicable laws. In addition to base compensation, this role is eligible to participate in our equity incentive and competitive benefits plans, including but not limited to: flexible PTO, Medical/Dental/Vision plan options, 401(k) with company match, flexible spending accounts, Teladoc Health and more.

The Data Science team at Asana is pivotal in fulfilling our mission by fostering a data-driven approach in shaping both our product and business strategies. In your role on the Marketing Data Science team, you will be the deepest technical expert responsible for using data and scientific techniques to design and build scalable, state-of-the-art solutions to enhance Asana’s marketing effectiveness. You will drive the technical roadmap for data science, collaborating with marketing leadership and the broader Asana data community to uncover new opportunities. You will provide technical leadership and hands-on mentorship, elevating the team's technical bar and influencing overall business strategy through best-in-class modeling and experimental design.

This role is based in our New York City office with an office-centric hybrid schedule. The standard in-office days are Monday, Tuesday, and Thursday. Most Asanas have the option to work from home on Wednesdays. Working from home on Fridays depends on the type of work you do and the teams with which you partner. If you're interviewing for this role, your recruiter will share more about the in-office requirements.

What you’ll achieve:

• Architect, design, and lead the technical execution for the Marketing Data Science roadmap, serving as the Solution Architect for all core projects including Media Mix Modeling (MMM), User Lifetime Value, Causal Inferences, Multi-touch Attribution, and Spend Optimization engines.
• Act as the primary technical subject matter expert for the Marketing Data Science team, setting the technical bar for modeling quality, code rigor, data pipeline architecture, and solution scalability.
• Collaborate with marketing leadership to pinpoint how data science can be further integrated into Asana's business approach.
• Provide hands-on technical mentorship and guidance to a team of data scientists at varying levels, helping them navigate complex modeling challenges, choose appropriate methodologies, and establish robust ML Ops.
• Develop and standardize MLOps tooling and processes that enable the team to deploy, monitor, and maintain multiple models in production efficiently and reliably.
• Research, prototype, and advocate for emerging capabilities and state-of-the-art models in the marketing data science space, demonstrating their potential benefits and leading their implementation.
• Take on a technical leadership role within the broader Asana Data Community, interacting with Data Engineering and Platform teams to influence the data and MLOps infrastructure required to support marketing data products.

About you:

• Bachelor Degree in Math, Statistics, Computer Science, Engineering a related quantitative field, or equivalent experience
• 6+ years of experience in a data science role, with 2+ years dedicated to technical leadership and mentorship of other data scientists, successfully driving the architecture and execution of large-scale production data science projects
• 4+ years of experience collaborating with Marketing functions on deep technical projects, with extensive experience designing, implementing, and deploying marketing models (e.g. MMM, LTV, MTA, Uplift)
• Expert-level knowledge in advanced statistical modeling, causal inference, experimental design and analysis, and machine learning techniques relevant to marketing effectiveness
• Proven track record developing, deploying, and maintaining scalable production ML solutions and data products
• Technical Stack: Expert proficiency in SQL and Python. Experience with MLOps tools (e.g., MLFlow), statistical languages (e.g., R), and distributed data processing systems (e.g., Spark, Redshift) is a plus
• Demonstrates curiosity about AI tools and emerging technologies, with a willingness to learn and leverage them to enhance productivity, collaboration, or decision-making.

What we’ll offer:

Our comprehensive compensation package plays a big part in how we recognize you for the impact you have on our path to achieving our mission. We believe that compensation should be reflective of the value you create relative to the market value of your role. To ensure pay is fair and not impacted by biases, we're committed to looking at market value which is why we check ourselves and conduct a yearly pay equity audit.

For this role, the estimated base salary range is between $202,000 - $282,000. The actual base salary will vary based on various factors, including market and individual qualifications objectively assessed during the interview process. The listed range above is a guideline, and the base salary range for this role may be modified.

In addition to base salary, your compensation package may include additional components such as equity, sales incentive pay (for most sales roles), and benefits. If you're interviewing for this role, speak with your Talent Acquisition Partner to learn more about the total compensation and benefits for this role.

We strive to provide equitable and competitive benefits packages that support our employees worldwide and include:

• Mental health, wellness & fitness benefits
• Career coaching & support
• Inclusive family building benefits
• Long-term savings or retirement plans
• In-office culinary options to cater to your dietary preferences 

These are just some of the benefits we offer, and benefits may vary based on role, country, and local regulations. If you're interviewing for this role, speak with your Talent Acquisition Partner to learn more about the total compensation and benefits for this role.

#LI-Hybrid #LI-AA1

The Opportunity:

Grow Therapy is seeking a Staff Data Scientist to deepen our understanding of our users and unlock the next era of our growth trajectory. In this role, you will lead cross-functional, data-driven initiatives and drive insights that get more people successfully onboarded and retained in online therapy, shape how we build our mobile app and digital experiences, and lead the charge on how AI is used alongside therapy. You will also develop and own marketing and attribution models that help us reach patients more effectively and empower our providers’ success. Your work will shape product strategy, experimentation, and long-term investment decisions across the company.

What You’ll Be Doing:

• Lead experimentation and metric development to understand user behavior, optimize onboarding and retention, and guide product direction across web and mobile experiences.
• Build predictive, attribution, and behavioral models that inform marketing strategy, therapist-matching workflows, and future AI-powered features.
• Partner closely with Product, Engineering, Design, and Operations to identify opportunities, quantify impact, and drive roadmap decisions with clear, actionable insights.
• Develop dashboards, analytical frameworks, and self-serve tools that scale data access and empower teams to answer their own product questions.
• Shape Grow Therapy’s AI strategy by designing evaluation frameworks, running model-level experiments, and identifying where AI can improve the patient and provider experience.

You’ll Be a Good Fit If:

• 8+ years of experience in a quantitative role applying analytics, experimentation, and modeling in a product-focused environment, including leading high-impact, cross-functional data science or analytics initiatives.
• Fluent in SQL and Python, with deep experience designing A/B tests, building models, and setting a high bar for translating complex data into clear recommendations for stakeholders at all levels.
• Proven experience owning and driving product or growth decisions through deep user understanding, funnel analytics, or marketplace dynamics, and doing so through influence as well as hands-on work.
• Comfortable navigating ambiguity and working cross-functionally in fast-moving environments where data science directly influences strategy, and aligning multiple teams around shared metrics and insights.
• Excited about improving access to mental health care and motivated by building AI-enhanced experiences that support therapists and patients.

Role Details:

Employment Type: Full Time, Exempt
Base Compensation: The base compensation range for this position is:

• Hybrid Commitment: $200,000–$250,000 USD USD Annually

This role can be hybrid (onsite from our NYC, San Francisco, or Seattle hub location three days per week: Tuesday, Wednesday, Thursday) or fully remote. Both arrangements include travel 2–3 times per year (e.g., company and department off-sites).

The Opportunity:

Grow Therapy is seeking a Senior/Staff Data Scientist to deepen our understanding of our users and unlock the next era of our growth trajectory. In this role, you will drive insights that get more people successfully onboarded and retained in online therapy, shape how we build our mobile app and digital experiences, and lead the charge on how AI is used alongside therapy. You will also develop marketing and attribution models that help us reach patients more effectively and empower our providers’ success. Your work will directly influence product strategy, experimentation, and long-term investment decisions across the company.

What You’ll Be Doing:

• Lead experimentation and metric development to understand user behavior, optimize onboarding and retention, and guide product direction across web and mobile experiences.
• Build predictive, attribution, and behavioral models that inform marketing strategy, therapist-matching workflows, and future AI-powered features.
• Partner closely with Product, Engineering, Design, and Operations to identify opportunities, quantify impact, and drive roadmap decisions with clear, actionable insights.
• Develop dashboards, analytical frameworks, and self-serve tools that scale data access and empower teams to answer their own product questions.
• Shape Grow Therapy’s AI strategy by designing evaluation frameworks, running model-level experiments, and identifying where AI can improve the patient and provider experience.

You’ll Be a Good Fit If: 

• 5+ years of experience in a quantitative role applying analytics, experimentation, and modeling in a product-focused environment.
• Fluent in SQL and Python, with experience designing A/B tests, building models, and translating complex data into clear recommendations.
  Experience driving product or growth decisions through deep user understanding, funnel analytics, or marketplace dynamics.
• Comfort navigating ambiguity and working cross-functionally in fast-moving environments where data science directly influences strategy.
• Excited about improving access to mental health care and motivated by building AI-enhanced experiences that support therapists and patients.

Role Details:

Employment Type: Full Time, Exempt
Base Compensation: The base compensation range for this position is:

• Hybrid Commitment: $182,000–$228,000 USD USD Annually

This role can be hybrid (onsite from our NYC, San Francisco, or Seattle hub location three days per week: Tuesday, Wednesday, Thursday) or fully remote. Both arrangements include travel 2–3 times per year (e.g., company and department off-sites).

As a Data Scientist, reporting to our Director of Analytics, you will play a critical strategic role in shaping Flex's future by guiding key analytics projects across our Go-To-Market, Marketing, Product, and Strategy teams. You will champion truth seeking across the business, working to bring clarity to some of Flex’s most challenging and complex areas. Through your technical expertise and mastery of data-led story telling, you improve our understanding of the business and where we can make improvements. The ideal candidate for this role is intensely curious, holds themselves to a high analytical bar, while simultaneously balancing analytical rigor with speed and ultimate business impact.

We currently have multiple roles open across Flex Analytics and are prioritizing analyst support for Product, Partnerships and GTM domains.

What you’ll do

• Improve our understanding of key Flex domains by creating scalable frameworks that connect dots across the business into easily interpretable outputs
• Proactively and effectively root cause data and performance issues across the business in order to ensure continued strong performance
• Create and improve dashboards, visualizations, and automations that help us scale analytics across Flex
• Work to establish clear causal links between actions and outcomes through experimentation and other relevant methods, where applicable
• Combine data and strategy - being that bridge between what the data is telling us and the implications for strategic priorities within different areas of the business
• Champion best practices, precise and consistent terminology, and robust documentation within Analytics and more broadly across the organization

Key qualifications

• 3+ years of experience using analytical and statistical methods to support and drive critical business outcomes
• Expert communicator who can break down complex technical concepts into simple business vernacular
• Experience across a variety of analytical domains that allow you to bring unique perspectives to solving hard problems
• Demonstrated success driving business strategy and outcomes from an analytics lens
• Advanced experience with SQL, business intelligence tools (e.g., Tableau, Mode, Looker, etc.)
• Experience with ETL processes, especially utilizing dbt and Snowflake
• Track record of using quantitative, analytical, and problem-solving skills to drive decisions
• Biased for action - knows when to not let perfect be the enemy of good enough, while also can effectively highlight the tradeoffs/risks of moving too quickly too soon
• Strong ownership mindset with a deep natural curiosity to dive into the details and understand how systems work

This role is hybrid with on-site expectations of 3 days per week in our New York Headquarters. 
We also have offices in the Bay Area and Salt Lake City

For candidates located in our Tier 1 markets (NYC/ SF), the base salary pay range for this role is $133,500—$165,000 USD.

For all other U.S. locations, Flex utilizes a geographic pay differential based on a cost of labor index. If you are located outside of the Tier 1 states listed above, your starting pay will be adjusted to align with the market conditions of your specific geographic zone.

As a Staff Data Scientist, reporting to our Director of Analytics, you will play a critical strategic role in shaping Flex's future by guiding key analytics projects across our Customer Support, Finance, Go-To-Market, Marketing, Product, and Strategy teams. You will champion truth seeking across the business, working to bring clarity to some of Flex’s most challenging and complex areas. Through your technical expertise and mastery of data-led story telling, you improve our understanding of the business and where we can make improvements. The ideal candidate for this role is intensely curious, holds themselves to a high analytical bar, while simultaneously balancing analytical rigor with speed and ultimate business impact.

We currently have multiple roles open across Flex Analytics and are prioritizing analyst support for Product, Partnerships and GTM domains.

What you’ll do

• Improve our understanding of key Flex domains by creating scalable frameworks that connect dots across the business into easily interpretable outputs
• Proactively and effectively root cause data and performance issues across the business in order to ensure continued strong performance
• Create and improve dashboards, visualizations, and automations that help us scale analytics across Flex
• Work to establish clear causal links between actions and outcomes through experimentation and other relevant methods, where applicable
• Combine data and strategy - being that bridge between what the data is telling us and the implications for strategic priorities within different areas of the business
• Champion best practices, precise and consistent terminology, and robust documentation within Analytics and more broadly across the organization

Key qualifications

• 5+ years of experience using analytical and statistical methods to support and drive critical business outcomes
• Expert communicator who can break down complex technical concepts into simple business vernacular
• Experience across a variety of analytical domains that allow you to bring unique perspectives to solving hard problems
• Demonstrated success driving business strategy and outcomes from an analytics lens
• Advanced experience with SQL, business intelligence tools (e.g., Tableau, Mode, Looker, etc.)
• Experience with ETL processes, especially utilizing dbt and Snowflake
• Track record of using quantitative, analytical, and problem-solving skills to drive decisions
• Biased for action - knows when to not let perfect be the enemy of good enough, while also can effectively highlight the tradeoffs/risks of moving too quickly too soon
• Strong ownership mindset with a deep natural curiosity to dive into the details and understand how systems work

This role is hybrid with on-site expectations of 3 days per week in our New York Headquarters. 
We also have offices in the Bay Area and Salt Lake City

Flex takes a market-based approach to pay, ensuring compensation is commensurate with a candidate's experience and our internal leveling guidelines. For candidates located in our Tier 1 markets (NYC/ SF), the base salary pay range for this role is $155,000—$194,000 USD.

For all other U.S. locations, Flex utilizes a geographic pay differential based on a cost of labor index. If you are located outside of the Tier 1 states listed above, your starting pay will be adjusted to align with the market conditions of your specific geographic zone. Please speak with your recruiter for additional information regarding the specific range for your location

About the role

As a Sr. Staff Data Scientist reporting to our Director of Analytics, you will play a critical strategic role in shaping Flex's future by guiding key analytics projects across our Product teams. You will champion truth seeking across the business, working to bring clarity to some of Flex’s most challenging and complex areas. Through your technical expertise and mastery of data-led story telling, you improve our understanding of the business and where we can make improvements. The ideal candidate for this role is intensely curious, holds themselves to a high analytical bar, while simultaneously balancing analytical rigor with speed and ultimate business impact.

What you’ll do

• Improve our understanding of key Flex domains by creating scalable frameworks that connect dots across the business into easily interpretable outputs
• Proactively and effectively root cause data and performance issues across the business in order to ensure continued strong performance
• Create and improve dashboards, visualizations, and automations that help us scale analytics across Flex
• Work to establish clear causal links between actions and outcomes through experimentation and other relevant methods, where applicable
• Combine data and strategy - being that bridge between what the data is telling us and the implications for strategic priorities within different areas of the business
• Champion best practices, precise and consistent terminology, and robust documentation within Analytics and more broadly across the organization

Key qualifications

• PhD, Masters, or Bachelor’s Degree with a quantitative focus (Economics, Finance, Computer Science, Mathematics, etc.)
• 8+ years of experience using analytical and statistical methods to support and drive critical business outcomes
• Expert communicator who can break down complex technical concepts into simple business vernacular
• Experience across a variety of analytical domains that allow you to bring unique perspectives to solving hard problems
• Demonstrated success driving business strategy and outcomes from an analytics lens
• Advanced experience with SQL, business intelligence tools (e.g., Tableau, Mode, Looker, etc.)
• Experience with ETL processes, especially utilizing dbt and Snowflake
• Track record of using quantitative, analytical, and problem-solving skills to drive decisions
• Biased for action - knows when to not let perfect be the enemy of good enough, while also can effectively highlight the tradeoffs/risks of moving too quickly too soon
• Strong ownership mindset with a deep natural curiosity to dive into the details and understand how systems work

This role is hybrid with on-site expectations of 3 days per week in our New York Headquarters or Hybrid from our San Francisco office. 

Tier 1 markets (NYC/ SF), the base salary pay range for this role is $194,000-240,000 USD

About the Role

As a Staff Data Scientist, you’ll be seen as a tech lead thought leader across company leaders and across Product, Engineering, Design, Operations, and Clinical teams. You will help the company be rigorous and thoughtful on how to best use data and apply statistical methods. You will bring an experienced perspective among the team to help them polish their thinking and ultimately inform their roadmaps, shape their thinking, and drive outcomes. You will be a highly engaged cross-functional partner that is sought out to solve challenges and will be able to bridge the gap between technical and non-technical stakeholders, upleveling those around you.

We’re a team of passionate, forward-thinking professionals eager to take on the challenge of the mental health crisis and play a formative role in providing life-saving solutions. If you’re inspired by our mission and energized by the opportunity to increase access to mental healthcare and impact millions of lives in a profound way, apply today.

Responsibilities

• Partner with Product, Engineering, Design, Clinical, Operations, and Machine Learning teams to turn ambiguous questions into clear analytical recommendations that influence strategy and decision-making.
• Build and establish models and analyses using appropriate statistical methods that identify key roadmap items to prioritize across the respective teams.
• Regularly influence leaders to accept recommendations and define the metrics that matter which will generate concrete and notable impact to the company’s mission.
• Partner closely with Analytics and Data Engineering to define key objectives for the team, design and scale repeatable frameworks, and enable broader self-serve use of existing tools & methods while keeping a high bar when it comes to analytical rigor.
• Be a causal inference expert with knowledge across several methodologies (e.g. Experimentation, Diff-in-Diff, IV, Propensity Score, etc.) and serve as a key stakeholder in enabling this thinking. 
• Form and vocalize opinions on how we should do things and gain trust among company leaders as a key partner they should feel empowered to reach out to for brainstorming and new initiatives.
• Serve as a tech lead for the Data Science org with strong mentorship of peers.  

Requirements

• 7+ years in data science/analytics roles, at least 3 of those years in a tech lead capacity.
• Demonstrated ability to navigate an ambiguous data environment, with start-up experience preferred related questions and provide approaches with appropriate statistical rigor to a wide variety of stakeholders. 
• Experience with modeling that led to clear and concrete adoption of recommendations and demonstrated visible impact
• Detailed knowledge of causal inference methods including (but not limited to): RCT, Diff-in-Diff, IPW, Propensity Score, Instrumental Variables, Variance Reduction, etc.
• Experience with leveraging LLMs on text to generate insights and amplify existing work.
• Strong technical proficiency: SQL, Python or R, modern ELT/ETL, OLAP databases (e.g., Snowflake), dbt, BI tools (e.g., Tableau, Hex), and experience setting up and scaling experimentation programs (A/B tests, causal inference, lift measurement).
• Excellent ability to communicate, collaborate, and influence business partners at all levels, but especially executives and those at Director+ levels.
• Bonus: Healthcare Experience, Start-up Experience
• Passion for mission-driven work in mental health or healthcare settings and a desire to apply your skills to improve outcomes
• Please note: candidates located within a 75-minute commute of our NYC office are expected to work onsite 4 days/week.

Benefits

Charlie Health is pleased to offer comprehensive benefits to all full-time, exempt employees. Read more about our benefits here.

The total target base compensation for this role will be between $190,000 and $270,000 per year at the commencement of employment. Please note, pay will be determined on an individualized basis and will be impacted by location, experience, expertise, internal pay equity, and other relevant business considerations. Further, cash compensation is only part of the total compensation package, which, depending on the position, may include stock options and other Charlie Health-sponsored benefits. #LI-hybrid

About the Role

As a Staff Data Scientist - Growth and Marketing, you’ll be seen as a thought leader among Growth, Marketing, and Revenue partners to help them be rigorous and thoughtful on how to best use data. You will bring an experienced perspective among the team to inform their roadmaps, shape their thinking, and drive outcomes. You will contribute directly to the growth of the company, via streamlining marketing measurement and approaches, leveraging statistical rigor via modelling & causal inference to teams, and via identifying new opportunities for continued and sustained impact across the org.

We’re a team of passionate, forward-thinking professionals eager to take on the challenge of the mental health crisis and play a formative role in providing life-saving solutions. If you’re inspired by our mission and energized by the opportunity to increase access to mental healthcare and impact millions of lives in a profound way, apply today.

Responsibilities

• Partner with Growth, Marketing, Revenue, and Operations to turn ambiguous business questions into clear analytical recommendations that influence strategy and decision-making
• Build and iterate on lead, account, and ICP scoring models that help reps prioritize efforts and partner with Ops on data-driven territory design
• Develop models to analyze successful high-performer rep patterns to optimize onboarding and productivity
• Architect and maintain a trusted marketing measurement framework (e.g. MMM, incrementality testing [geo tests, holdouts, lift studies]), & align on consistent metric definitions (e.g. CAC, conversion, retention)
• Develop spend optimization & channel allocation frameworks that account for saturation, diminishing returns, and the tradeoff between short-term acquisition and long-term outcomes
• Apply causal inference methods (DiD, synthetic controls, quasi-experiments) to own channel-level incrementality measurement, with clear decision rules & guardrails for each approach
• Design and scale repeatable experimentation frameworks for channel, creative, and audience testing, increasing velocity without compromising rigor

Requirements

• 7+ years in data science/analytics roles, at least 3 of those years with Marketing, Sales, and/or Revenue teams
• Demonstrated ability to navigate an ambiguous data environment, with start-up experience preferred related questions and provide approaches with appropriate statistical rigor to a wide variety of stakeholders
• Familiarity with CRMs (e.g. Salesforce), Marketing Platforms, and the metrics / terminology associated with them (CAC, Opps, etc.) 
• Experience with modeling work that spans at least some of: MMM, LTV, Lead Scoring, Attribution Modeling, and Segmentation
• Strong technical proficiency: SQL, Python or R, modern ELT/ETL, OLAP databases (e.g., Snowflake), dbt, BI tools (e.g., Tableau, Hex), and experience setting up and scaling experimentation programs (A/B tests, causal inference, lift measurement)
• Excellent ability to communicate, collaborate, and influence business partners at all levels
• Bonus: Healthcare Experience, Start-up Experience
• Passion for mission-driven work in mental health or healthcare settings and a desire to apply your skills to improve outcomes
• Please note: candidates located within a 75-minute commute of our NYC office are expected to work onsite 4 days/week

Benefits

Charlie Health is pleased to offer comprehensive benefits to all full-time, exempt employees. Read more about our benefits here.

Additional Information 

The total target base compensation for this role will be between $190,000 and $270,000 per year at the commencement of employment. Please note, pay will be determined on an individualized basis and will be impacted by location, experience, expertise, internal pay equity, and other relevant business considerations. Further, cash compensation is only part of the total compensation package, which, depending on the position, may include stock options and other Charlie Health-sponsored benefits. #LI-hybrid

About the Role

As a Staff Data Scientist - Growth and Marketing, you’ll be seen as a thought leader among Growth, Marketing, and Revenue partners to help them be rigorous and thoughtful on how to best use data. You will bring an experienced perspective among the team to inform their roadmaps, shape their thinking, and drive outcomes. You will contribute directly to the growth of the company, via streamlining marketing measurement and approaches, leveraging statistical rigor via modelling & causal inference to teams, and via identifying new opportunities for continued and sustained impact across the org.

We’re a team of passionate, forward-thinking professionals eager to take on the challenge of the mental health crisis and play a formative role in providing life-saving solutions. If you’re inspired by our mission and energized by the opportunity to increase access to mental healthcare and impact millions of lives in a profound way, apply today.

Responsibilities

• Partner with Growth, Marketing, Revenue, and Operations to turn ambiguous business questions into clear analytical recommendations that influence strategy and decision-making
• Build and iterate on lead, account, and ICP scoring models that help reps prioritize efforts and partner with Ops on data-driven territory design
• Develop models to analyze successful high-performer rep patterns to optimize onboarding and productivity
• Architect and maintain a trusted marketing measurement framework (e.g. MMM, incrementality testing [geo tests, holdouts, lift studies]), & align on consistent metric definitions (e.g. CAC, conversion, retention)
• Develop spend optimization & channel allocation frameworks that account for saturation, diminishing returns, and the tradeoff between short-term acquisition and long-term outcomes
• Apply causal inference methods (DiD, synthetic controls, quasi-experiments) to own channel-level incrementality measurement, with clear decision rules & guardrails for each approach
• Design and scale repeatable experimentation frameworks for channel, creative, and audience testing, increasing velocity without compromising rigor

Requirements

• 7+ years in data science/analytics roles, at least 3 of those years with Marketing, Sales, and/or Revenue teams
• Demonstrated ability to navigate an ambiguous data environment, with start-up experience preferred related questions and provide approaches with appropriate statistical rigor to a wide variety of stakeholders
• Familiarity with CRMs (e.g. Salesforce), Marketing Platforms, and the metrics / terminology associated with them (CAC, Opps, etc.) 
• Experience with modeling work that spans at least some of: MMM, LTV, Lead Scoring, Attribution Modeling, and Segmentation
• Strong technical proficiency: SQL, Python or R, modern ELT/ETL, OLAP databases (e.g., Snowflake), dbt, BI tools (e.g., Tableau, Hex), and experience setting up and scaling experimentation programs (A/B tests, causal inference, lift measurement)
• Excellent ability to communicate, collaborate, and influence business partners at all levels
• Bonus: Healthcare Experience, Start-up Experience
• Passion for mission-driven work in mental health or healthcare settings and a desire to apply your skills to improve outcomes
• Please note: candidates located within a 75-minute commute of our NYC office are expected to work onsite 4 days/week

Benefits

Charlie Health is pleased to offer comprehensive benefits to all full-time, exempt employees. Read more about our benefits here.

Additional Information 

The total target base compensation for this role will be between $190,000 and $270,000 per year at the commencement of employment. Please note, pay will be determined on an individualized basis and will be impacted by location, experience, expertise, internal pay equity, and other relevant business considerations. Further, cash compensation is only part of the total compensation package, which, depending on the position, may include stock options and other Charlie Health-sponsored benefits. #LI-hybrid

About the Role

As a Staff Data Scientist, you’ll be seen as a tech lead thought leader across company leaders and across Product, Engineering, Design, Operations, and Clinical teams. You will help the company be rigorous and thoughtful on how to best use data and apply statistical methods. You will bring an experienced perspective among the team to help them polish their thinking and ultimately inform their roadmaps, shape their thinking, and drive outcomes. You will be a highly engaged cross-functional partner that is sought out to solve challenges and will be able to bridge the gap between technical and non-technical stakeholders, upleveling those around you.

We’re a team of passionate, forward-thinking professionals eager to take on the challenge of the mental health crisis and play a formative role in providing life-saving solutions. If you’re inspired by our mission and energized by the opportunity to increase access to mental healthcare and impact millions of lives in a profound way, apply today.

Responsibilities

• Partner with Product, Engineering, Design, Clinical, Operations, and Machine Learning teams to turn ambiguous questions into clear analytical recommendations that influence strategy and decision-making.
• Build and establish models and analyses using appropriate statistical methods that identify key roadmap items to prioritize across the respective teams.
• Regularly influence leaders to accept recommendations and define the metrics that matter which will generate concrete and notable impact to the company’s mission.
• Partner closely with Analytics and Data Engineering to define key objectives for the team, design and scale repeatable frameworks, and enable broader self-serve use of existing tools & methods while keeping a high bar when it comes to analytical rigor.
• Be a causal inference expert with knowledge across several methodologies (e.g. Experimentation, Diff-in-Diff, IV, Propensity Score, etc.) and serve as a key stakeholder in enabling this thinking. 
• Form and vocalize opinions on how we should do things and gain trust among company leaders as a key partner they should feel empowered to reach out to for brainstorming and new initiatives.
• Serve as a tech lead for the Data Science org with strong mentorship of peers.  

Requirements

• 7+ years in data science/analytics roles, at least 3 of those years in a tech lead capacity.
• Demonstrated ability to navigate an ambiguous data environment, with start-up experience preferred related questions and provide approaches with appropriate statistical rigor to a wide variety of stakeholders. 
• Experience with modeling that led to clear and concrete adoption of recommendations and demonstrated visible impact
• Detailed knowledge of causal inference methods including (but not limited to): RCT, Diff-in-Diff, IPW, Propensity Score, Instrumental Variables, Variance Reduction, etc.
• Experience with leveraging LLMs on text to generate insights and amplify existing work.
• Strong technical proficiency: SQL, Python or R, modern ELT/ETL, OLAP databases (e.g., Snowflake), dbt, BI tools (e.g., Tableau, Hex), and experience setting up and scaling experimentation programs (A/B tests, causal inference, lift measurement).
• Excellent ability to communicate, collaborate, and influence business partners at all levels, but especially executives and those at Director+ levels.
• Bonus: Healthcare Experience, Start-up Experience
• Passion for mission-driven work in mental health or healthcare settings and a desire to apply your skills to improve outcomes
• Please note: candidates located within a 75-minute commute of our NYC office are expected to work onsite 4 days/week.

Benefits

Charlie Health is pleased to offer comprehensive benefits to all full-time, exempt employees. Read more about our benefits here.

The total target base compensation for this role will be between $190,000 and $270,000 per year at the commencement of employment. Please note, pay will be determined on an individualized basis and will be impacted by location, experience, expertise, internal pay equity, and other relevant business considerations. Further, cash compensation is only part of the total compensation package, which, depending on the position, may include stock options and other Charlie Health-sponsored benefits. #LI-hybrid

The Mission

As CoreWeave continues to solidify its position as the Essential Cloud for AI, we are seeking a visionary VP of Product, Research Training Infrastructure. This executive leader will own the product strategy and engineering execution for the services that power the most ambitious AI research labs in the world. You will bridge the gap between "the metal" and the researcher, delivering a seamless, high-performance environment where frontier models are born.

The Role: Architect of the AI Factory

You will lead the product strategy of our Research Training Stack, focusing on the specialized orchestration, evaluation, and iteration tools required for massive-scale pre-training and post-training. This is a mission-critical role at the intersection of high-performance computing (HPC) and cloud-native agility.

Core Responsibilities

• Frontier Orchestration: Oversee the evolution of SUNK (Slurm on Kubernetes) to provide researchers with deterministic, bare-metal performance through a cloud-native interface.
• Holistic Training Services: Beyond Slurm, drive the development of next-generation orchestrators and automated training-based evaluation frameworks that ensure model quality throughout the lifecycle.
• Post-Training Excellence: Build the infrastructure required for sophisticated Reinforcement Learning (RL) and RLHF pipelines, enabling labs to refine foundation models with maximum efficiency.
• Customer Advocacy: Act as the primary technical partner for lead researchers at global AI labs, translating their "future-state" requirements into actionable product roadmaps.

Requirements: Deep Research & Infrastructure Mastery

• Proven Leadership: 15+ years of experience in engineering leadership, with at least 5+ years managing large-scale infrastructure at a top-tier research lab or an AI-native cloud provider.
• Domain Expertise: Deep, hands-on knowledge of Slurm, Kubernetes, and the specific networking requirements (InfiniBand/RDMA) for distributed training clusters.
• Research Mindset: You likely come from a background supporting frontier model research (pre-training and post-training) and understand the "pain points" of a research scientist.
• Scaling Experience: A track record of delivering mission-critical services on multi-thousand GPU clusters (H100/Blackwell/Rubin architectures).
• Strategic Vision: Ability to define "what’s next" in the AI stack, from automated RL loops to specialized sandbox environments.

Why CoreWeave?

In 2026, CoreWeave is the foundation of the largest infrastructure buildout in human history. We are building AI Factories, not just data centers.

• Silicon-Up Innovation: Work directly with the latest NVIDIA architectures.
• Impact: You will be the architect of the environment that enables the next new discovery.

Velocity: We move at the speed of the researchers we support, bypassing legacy cloud bottlenecks to deliver raw power.

The base salary range for this role is $233,000 to $341,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).