Reddit has a flexible first workforce. At Reddit we continue to grow our teams with the best talent. We're completely remote friendly and will continue to be after the pandemic.

Advertising is Reddit’s primary revenue driver and we have an ambitious goal to turn it into a massive business. Although several large digital ad platforms already exist, advertisers are being increasingly drawn to Reddit because of our passionate communities. We believe our community-centric platform has created the opportunity for Reddit to build a highly differentiated ads business.

About the Ads Data Science Team:

The Ads Data Science team at Reddit leverages data to maximize advertiser value on Reddit through robust data foundations, metrics, and strategic insights generated through experimentation and cutting-edge DS methods. 

We work on a wide range of challenging problems in the areas of ads targeting, relevance modeling, auction optimization, measurement, and user experience. We are a highly collaborative team of passionate data scientists and engineers who are constantly pushing the boundaries of what's possible with machine learning and statistical modeling.

About the Role:

We are looking for a highly motivated and experienced Data Scientist to join our growing Ads Data Science team. As a Data Scientist, you will play a key role in developing as well as applying cutting-edge DS models/methods to improve our understanding of the dynamics that drive the success of our advertising platform, and identify opportunities to accelerate that success. You will work closely with stakeholders across product, engineering, sales, marketing, and data to identify opportunities, define metrics, and build solutions that drive significant impact for Reddit.

Responsibilities:

• Analyze large-scale datasets to identify trends, patterns, and insights that can be used to improve the effectiveness of our advertising platform
• Develop ML models & DS methods to for improved anomaly detection, prediction, pattern recognition
• Communicate findings and recommendations to stakeholders across the organization
• Collaborate with product, engineering, sales, and marketing partners to define product and program requirements and translate them into data science solutions
• Stay up-to-date on the latest advancements in machine learning and data science

Qualifications:

• Advanced degree (Masters or Ph.D.) in a quantitative field such as: Statistics, Mathematics, Physics, Economics, or Operations Research
• For M.S. holders: 3+ years of industry experience in applied science or data science roles
• For Ph.D. holders: 2+ years of industry experience in applied science or data science roles
• Strong understanding of statistical modeling, machine learning algorithms, causal inference and experimental design
• Experience with large-scale data processing and analysis using tools such as Spark, Hadoop, or Hive; knowledge of BigQuery a plus
• Proficiency in Python or R and experience with machine learning libraries such as scikit-learn, TensorFlow, or PyTorch
• Experience with SQL and relational databases
• Excellent communication and presentation skills
• Passion for Reddit and the online advertising industry

Bonus Points:

• Experience with online advertising and ad tech
• Experience with causal inference and A/B testing
• Contributions to open-source projects or publications in relevant conferences or journals

Benefits:

• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Comprehensive Medical Benefits & Health Care Spending Account
• Registered Retirement Savings Plan with matching contributions
• Income Replacement Programs
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave

#LI-Remote

Reddit has a flexible first workforce. At Reddit we continue to grow our teams with the best talent. We're completely remote friendly and will continue to be after the pandemic.

Advertising is Reddit’s primary revenue driver and we have an ambitious goal to turn it into a massive business. Although several large digital ad platforms already exist, advertisers are being increasingly drawn to Reddit because of our passionate communities. We believe our community-centric platform has created the opportunity for Reddit to build a highly differentiated ads business.

About the Ads Data Science Team:

The Ads Data Science team at Reddit leverages data to maximize advertiser value on Reddit through robust data foundations, metrics, and strategic insights generated through experimentation and cutting-edge DS methods. 

We work on a wide range of challenging problems in the areas of ads and platform measurement, campaign and creative management, advertiser growth and retention, monetization, and the intersection of brand and community engagement. We are a highly collaborative team of passionate data scientists and engineers who are constantly pushing the boundaries of what's possible with machine learning and statistical modeling.

About the Role:

We are looking for a highly motivated and experienced Senior Data Scientist to join our growing Ads Data Science team. As a Senior Data Scientist, you will play a key role in developing as well as applying cutting-edge DS models/methods to improve the adoption and performance of our advertising platform through data-driven insights. You will work closely with product managers, engineers, and other data scientists to identify opportunities, define metrics, and build solutions that drive significant impact for Reddit.

Responsibilities:

• Design, develop, and apply DS solutions to inform improvements in advertiser experience and Reddit's ad platform
• Analyze large-scale datasets to identify trends, patterns, and insights that can be used to improve the effectiveness of our advertising platform
• Collaborate with product managers and engineers to define product requirements and translate them into data science solutions
• Develop ML models & DS methods to improve anomaly detection, prediction, & pattern recognition 
• Communicate findings and recommendations to stakeholders across the organization
• Stay up-to-date on the latest advancements in machine learning and data science
• Mentor and guide junior data scientists on the team

Qualifications:

• Advanced degree (Masters or Ph.D.) in a quantitative field such as: Statistics, Mathematics, Physics, Economics, or Operations Research
• For M.S. holders: 5+ years of industry experience in applied science or data science roles
• For Ph.D. holders: 4+ years of industry experience in applied science or data science roles
• Platform experience and a deep understanding of the ads ecosystem
• Strong understanding of statistical modeling, machine learning algorithms, causal inference and experimental design
• Experience with large-scale data processing and analysis using tools such as Spark, Hadoop, or Hive; knowledge of BigQuery a plus
• Proficiency in Python or R and experience with machine learning libraries such as scikit-learn, TensorFlow, or PyTorch
• Experience with SQL and relational databases
• Excellent communication and presentation skills
• Passion for Reddit and the online advertising industry

Bonus Points:

• Experience with online advertising and ad tech
• Experience with causal inference and A/B testing
• Contributions to open-source projects or publications in relevant conferences or journals

Benefits:

• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Comprehensive Medical Benefits & Health Care Spending Account
• Registered Retirement Savings Plan with matching contributions
• Income Replacement Programs
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave

#LI-Remote

Reddit has a flexible first workforce. At Reddit we continue to grow our teams with the best talent. We're completely remote friendly and will continue to be.

Advertising is Reddit’s primary revenue driver and we have an ambitious goal to turn it into a massive business. Although several large digital ad platforms already exist, advertisers are being increasingly drawn to Reddit because of our passionate communities. We believe our community-centric platform has created the opportunity for Reddit to build a highly differentiated ads business.

About the Ads Data Science Team:

The Ads Data Science team at Reddit leverages data to maximize advertiser value on Reddit through robust data foundations, metrics, and strategic insights generated through experimentation and cutting-edge DS methods. 

We work on a wide range of challenging problems in the areas of ads and platform measurement, campaign and creative management, advertiser growth and retention, monetization, and the intersection of brand and community engagement. We are a highly collaborative team of passionate data scientists and engineers who are constantly pushing the boundaries of what's possible with machine learning and statistical modeling.

About the Role:

Reddit’s Ads Data Science team is looking for a highly experienced Staff Data Scientist to advance the intelligence powering the advertiser experience on Reddit. In this role, you'll take deep ownership of a high-impact problem space within advertising, specializing in measurement, identity, and signal quality.

This is a high-impact, high-autonomy role where you'll influence strategic direction, set a high technical bar, and drive cross-functional initiatives across one or more critical focus areas in the Ads organization.

Responsibilities:

• Design the Future of Ads Identity: Develop/employ probabilistic models for identity resolution. Design the methodology that links on-platform and off-platform actions to maximize addressability while honoring privacy.
• Advance Lift Methodologies & Experimentation: Own the statistical rigor behind Reddit’s Brand and Conversion Lift products. Innovate experimental design and develop infrastructure that supports large-scale, high-velocity, low-bias testing for advertisers.
• Maximize Signal for Predictive Performance: Define the strategy for new signal sources. You will mathematically quantify the value of these signals and work with modeling teams to incorporate them into predictive models, directly improving bidding efficiency and ROAS.
• Define Ground Truth & Evaluation Frameworks: Solve the industry-wide challenge of validating identity and measurement. Design the objective functions and truth sets used to train our models and measure the incremental impact of our identity graph.
• Lead Through Cross-Functional and Technical Influence: Collaborate deeply with engineering, product, and sales to align on strategic goals, translate insights into action, and drive execution. Set a high technical bar by mentoring others and championing best practices across modeling, experimentation, and measurement.

Qualifications:

• Required:
  • Advanced degree (Master's or Ph.D.) in Statistics, Mathematics, Physics, Economics, or Operations Research
  • For M.S. holders: 10+ years of industry experience in applied science or data science roles
  • For Ph.D. holders: 6+ years of industry experience in applied science or data science roles
  • Deep understanding of the ads ecosystem
  • Demonstrated expertise in at least one of the following areas:
    • Measurement & Experimentation at Scale (with focus on lift and attribution)
    • Identity Graph Creation & Resolution Methodology and Infrastructure
    • Predictive Modeling with Signal Loss
  • Advanced proficiency in statistical programming (Python or R) and SQL
  • Experience with machine learning or optimization techniques
  • Strong understanding of experimental design, causal inference, or A/B testing methodologies
  • Exceptional problem-solving and communication skills, with a track record of influencing product and engineering partners
  • Experience working in fast-paced, ambiguous environments with cross-functional teams

Benefits:

• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Comprehensive Medical Benefits & Health Care Spending Account
• Registered Retirement Savings Plan with matching contributions
• Income Replacement Programs
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave

#LI-Remote

What do we need

We are seeking a Scientific Technical Solutions Lead to join the team developing scientific Solutions built on Dotmatics LUMA, a next-generation, multi-tenant, AI-powered data management and workflow automation for multimodal scientific discovery. As a Scientific technical lead on the team, you will work closely with other Solution Architects, product management, engineering teams, and external stakeholders to deliver exceptional product capabilities via Luma Solutions.

Your focus will be on designing and implementing the configuration necessary to realise product vision and on ensuring that this configuration follows best practices within the Life Sciences and Chemical & Material space.

This role offers the opportunity to shape a foundational part of the modern lab—helping scientists work faster, smarter, and with greater impact.

In this role you will get to:

• Design configuration: Collaborate with Product to determine application structure, define entity/data models, specify task workflows (Scientific Workflows) , and define desired data visualization experiences
• Develop configuration: Create registration entities, data flows, task templates, data visualization experiences, and other configurations in support of developing Luma Solutions that will be delivered as part of the product
• Test and troubleshoot configuration: Perform in-team testing of configuration and assist is handing test cases to the QA group for release testing. Troubleshoot configuration to understand why it is not producing desired results.
• Contribute to documentation: Write draft documentation of configuration and workflows and collaborate with the Documentation team to develop final customer-facing documentation
• Train internal and external users: Train members of other Dotmatics teams and early customers on how to use Luma Solutions configuration and the workflows it supports
• Provide input into new product features: Use new product features in Luma Solutions and provide feedback on the experience, participate in user experience testing, and provide input to the Product team about features that will enable the best Solutions
• Champion quality and best practices: Actively participate in the development of best practices for Luma configuration, and ensure these best practices are followed in the development of Luma Solutions

We are looking for people who have 10+ years of hands-on experience building and delivering informatics solutions for Life Sciences customers. The ideal candidate holds a Bachelor's or advanced degree in Life Sciences, Data Science, Chemistry, Biology, Bioinformatics, or a related technical discipline — and brings a genuine passion for quality and continuous improvement, fueled by a drive to solve problems that truly matter.

The key skills we are looking for: 

• Life Science Industry experience, either as a Scientific Solution Architect, technical application Scientist or a Scientist who has been heavily involved with designing products or technical applications
• Advanced expertise in data modeling, with a proven ability to design robust, scalable data structures
• Expert-level proficiency in configuring low-code platforms and designing end-to-end workflows
• Demonstrated success delivering scientific data platforms or tools spanning data modeling, ETL, APIs, and BI/reporting
• Expert-level SQL skills, with strong working knowledge of Python and JSON
• Deep experience integrating tools and modules to architect complete, cohesive solutions
• Strong analytical ability to translate product requirements into precise technical configuration designs
• Excellent communication skills — able to distill complex technical concepts for both technical and non-technical audiences at all levels
• Experience running and/or supporting scientific workflows in a production or research environment

You may also have 

• Experience running scientific test cases
• Understanding of software testing and or SDLC
• Working knowledge of Databricks
• Drug Discovery or Biotechnology industries or Other scientific Industries

About the Job

Do you get excited when a messy, ambiguous business problem finally yields to the right model? Do you think in systems, speak fluently across the technical-business divide, and want your work to do more than sit in a notebook — you want it to actually ship, scale, and matter?

With over 2,000 employees, 36 offices on five continents, and world-class clients like Samsung, L'Oréal, and Mattel, Artefact is a consulting firm that transforms data into measurable value and business impact. We're growing our Canadian presence and want you to join us as a technical cornerstone of our founding team in Canada.

Who We Are

Founded and headquartered in Paris, Artefact is a next-generation consulting firm specializing in data, analytics, and AI consulting — dedicated to transforming data into business impact across the entire value chain of organizations. We don't just advise; we build, implement, and deliver results our clients can measure.

We have 2,000 employees across 36 offices focused on accelerating digital transformation for some of the world's most recognizable brands. Our state-of-the-art data technologies, lean AI agile methodologies, and cohesive teams of elite business consultants, data analysts, data scientists, data engineers, and digital experts are all laser-focused on delivering real value to every client. We design data-driven solutions tailored to each client's specific needs — always conceived with a business-first mindset and delivered with tangible, measurable results. Our expertise is built on deep AI knowledge acquired through 1,000+ client engagements across the globe.

Find out more at artefact.com.

What You Will Be Doing

As a Senior Data Scientist, you'll be the technical engine behind some of our most complex and consequential client engagements in Canada. You'll move fluidly between data exploration, model development, and executive communication — bringing scientific rigor to business problems and translating results into strategies that clients actually implement.

This isn't a role where you hand off findings and walk away. You'll be embedded with clients across Canada's most dynamic sectors — financial services, retail, CPG, telecoms, media, and the public sector — co-owning outcomes and ensuring that the models you build don't just perform in a test environment, they create real, lasting impact in production. You'll also be a technical anchor for our Canadian team, setting standards, mentoring junior data scientists, and contributing to the methodologies that define Artefact's edge in this market.

Key responsibilities include:

• Designing and building end-to-end machine learning and statistical models that solve high-stakes business problems — from framing the question to deploying the solution
• Conducting rigorous exploratory data analysis to uncover patterns, anomalies, and opportunities that inform both technical and strategic decisions
• Translating complex model outputs and analytical findings into clear, compelling narratives for senior client stakeholders — making the technical accessible without dumbing it down
• Partnering with client teams and data engineers to ensure models are production-ready, scalable, and built on clean, reliable data pipelines
• Defining the analytical approach for client engagements — selecting the right methods, tools, and frameworks for the problem at hand, not just the ones you're most comfortable with
• Contributing to new business proposals — helping articulate Artefact's technical capabilities and translating data science into clear client value
• Developing thought leadership and internal methodologies — publishing research, building reusable frameworks, and raising the technical bar across the Canadian practice
• Mentoring junior data scientists and analysts, actively investing in the team's technical depth and growth

What We Are Looking For

We want someone who is as comfortable whiteboarding a modeling strategy with a client's Chief Analytics Officer as they are debugging a pipeline before a critical delivery. You've shipped models that people actually use. You've sat in rooms where the business stakes were real, and you've delivered. You know the difference between a technically elegant solution and a practically useful one — and you always choose useful.

You'll arrive ready with the following:

• 4–7 years of hands-on experience in data science, machine learning, or advanced analytics — with a demonstrable track record of end-to-end model delivery in a client-facing or high-stakes business environment
• Advanced degree (MSc or PhD) in a quantitative field — statistics, mathematics, computer science, engineering, or equivalent; strong undergraduate candidates with exceptional experience will be considered
• Expert-level proficiency in Python and/or R; you write clean, maintainable, production-quality code
• Deep expertise in machine learning and statistical modeling — regression, classification, clustering, time series, NLP, recommendation systems, and/or deep learning, depending on your specialization
• Strong command of SQL and experience working with large-scale datasets across cloud platforms (GCP, AWS, or Azure)
• Experience with MLOps practices — model versioning, monitoring, deployment pipelines, and productionization — is a significant differentiator
• Exceptional communication skills — you can explain a gradient boosting model to a CFO and a business case to an ML engineer, and both conversations land
• Demonstrated ability to lead technical workstreams and mentor junior team membersBilingualism (English/French) is a significant asset, particularly for client engagement in Quebec and federal government contexts
• Consulting or client-facing experience is highly desirable; the ability to manage ambiguity, scope problems, and deliver under pressure is essential
• Familiarity with Canada's data privacy regulatory landscape — PIPEDA, Quebec's Law 25, and sector-specific frameworks in financial services and healthcare — is a meaningful differentiator
• Exposure to marketing analytics, customer analytics, fraud detection, risk modeling, or demand forecasting within Canadian financial services, retail, or CPG is a strong asset

Why You Should Join Us

At this level, you're not just looking for a job — you're looking for the right environment to do your best work. Here's what makes Artefact different:

Variety that keeps you sharp. In a product role, you go deep on one problem. Here, you'll work across Canada's most dynamic industries and business functions — building the kind of range that makes exceptional data scientists truly exceptional. No two engagements are the same.

Your work actually ships. We have a relentless focus on adoption and impact. The models you build are designed from day one to live in production, influence decisions, and create measurable value — not to gather dust in a final report.

A global technical community, with local roots. With 2,000 data and AI specialists across 36 offices worldwide, you'll have access to some of the most sophisticated data science thinking on the planet — while building something meaningful right here in Canada. You'll bring global best practices to the Canadian market and contribute Canadian insights back to the world.

Founding team energy. We're building our Canadian practice, which means the standards, culture, and technical identity you help create now will define Artefact in this market for years to come. That's a rare opportunity at any stage of a career.

We are united by our values and strengthened by our hybrid expertise:

There is always a way — We're builders, diggers, and makers. Ideas only count if they get executed.
Client trust is won in the field — We show up where it matters, working side by side with our clients to solve what's real.
If not used, it is useless — We build for adoption and impact. True brilliance is measured by what changes, not what's delivered.
If not shared, our work is not done — Sharing knowledge is how we close the loop — for our clients and for each other.
We learn every day — In a field moving at the speed of light, intellectual curiosity isn't optional. It's survival.

Role Description

How many times do you get the opportunity to be on the ground floor of a big and important mission? What if you could be one of the top contributors defining the mission, guiding our teams, and influencing the direction of Dropbox’s AI-first journey? As a Senior Data Scientist for this new division, you will get to do exactly that. You will join a team of top-tier data scientists and become an integral part of the product organization, helping to scale this new business.

Joining on the ground floor of this startup team, you’ll partner directly with Data Science, Product, Engineering and Design leadership to shape the product roadmap, foster a top-tier, data-informed culture, and drive real AI/ML impact and execution along the way!

Responsibilities

• Partner with Product Engineers and Data Engineers to build the reliable, efficient, and scalable data foundations, tools, and processes to drive our AI/ML capabilities’ long-term growth
• Leverage data-driven insights to proactively identify most impactful opportunities, and directly influence product roadmaps and strategies for Dash Chat and Search
• Perform exploratory and deep-dive analysis to understand user workflows and engagement patterns on AI features, propose hypothesis, and design & execute experiments with great rigor and efficient data techniques
• Translate complex data insights into implications and recommendations for the business via excellent communication skills, both verbal and written
• Identify what matters most and prioritize ruthlessly for the area you will own
• Contribute to a culture of strong technical ownership, partner with DS leadership to keep evolving DS working model and elevate DS impact
• Work with cross-functional teams (including Product, Engineering, Design, User Research, and senior executives) to rapidly execute and iterate

Requirements

• Bachelors’ or above in quantitative discipline: Statistics, Applied Mathematics, Economics, Computer Science, Engineering, or related field
• 7+ years experience of leveraging data-driven analysis to influence product roadmap and business decision, preferably in a tech company
• Proven track record of being able to work independently, driver measurable business impact, and proactively engage with business stakeholders with minimal direction  
• Proficiency in SQL, Python or other programming/scripting languages
• Deep understanding of statistical analysis, experimentation design, and common analytical techniques like regression, decision trees
• Ability to provide data insights and recommendations for 0→1 product even when sample size is small
• Strong verbal and written communication skills

Preferred Qualifications

• Experience in startups or building 0→1 products
• Expertise in using data to inform AI/ML product development
• Background in SaaS product

Role Description

How many times do you get the opportunity to be on the ground floor of a big and important mission? What if you could be one of the top contributors defining the mission, guiding our teams, and influencing the direction of Dropbox’s AI-first journey? As a Senior Data Scientist for this new division, you will get to do exactly that. You will join a team of top-tier data scientists and become an integral part of the product organization, helping to scale this new business.

Joining on the ground floor of this startup team, you’ll partner directly with Data Science, Product, Engineering and Design leadership to shape the product roadmap, foster a top-tier, data-informed culture, and drive real AI/ML impact and execution along the way!

Responsibilities

• Partner with Product Engineers and Data Engineers to build the reliable, efficient, and scalable data foundations, tools, and processes to drive our AI/ML capabilities’ long-term growth
• Leverage data-driven insights to proactively identify most impactful opportunities, and directly influence product roadmaps and strategies for Dash Chat and Search
• Perform exploratory and deep-dive analysis to understand user workflows and engagement patterns on AI features, propose hypothesis, and design & execute experiments with great rigor and efficient data techniques
• Translate complex data insights into implications and recommendations for the business via excellent communication skills, both verbal and written
• Identify what matters most and prioritize ruthlessly for the area you will own
• Contribute to a culture of strong technical ownership, partner with DS leadership to keep evolving DS working model and elevate DS impact
• Work with cross-functional teams (including Product, Engineering, Design, User Research, and senior executives) to rapidly execute and iterate

Requirements

• Bachelors’ or above in quantitative discipline: Statistics, Applied Mathematics, Economics, Computer Science, Engineering, or related field
• 7+ years experience of leveraging data-driven analysis to influence product roadmap and business decision, preferably in a tech company
• Proven track record of being able to work independently, driver measurable business impact, and proactively engage with business stakeholders with minimal direction  
• Proficiency in SQL, Python or other programming/scripting languages
• Deep understanding of statistical analysis, experimentation design, and common analytical techniques like regression, decision trees
• Ability to provide data insights and recommendations for 0→1 product even when sample size is small
• Strong verbal and written communication skills

Preferred Qualifications

• Experience in startups or building 0→1 products
• Expertise in using data to inform AI/ML product development
• Background in SaaS product

Role Description

We're looking for a Data Scientist to partner with product, engineering, and design teams to answer key questions about how to grow revenue, optimize product, scale and monetize the business, and launch high-impact initiatives. We solve challenging problems and boost business growth through a deep understanding of user behaviors with applied analytics techniques and business insights. An ideal candidate should have robust knowledge of consumer lifecycle and behavior analysis, customer segmentation, digital campaigns, monetization analytics and business operations for a SaaS company.  

Responsibilities

• Develop a deep understanding of customer journey phases and key business metrics
• Perform analytical deep-dives to analyze problems and opportunities, identify the hypothesis and design & execute experiments
• Inform future experimentation design and roadmaps by performing exploratory analysis to understand user engagement behavior and derive insights
• Create personalized segmentation strategies leveraging propensity models to enable targeting of offers and experiences based on user attributes 
• Identify key trends and build automated reporting & executive-facing dashboards to track the progress of acquisition, monetization, and engagement trends. 
• Extract actionable insights through analyzing large, complex, multi-dimensional customer behavior data sets 
• Monitor and analyze a high volume of experiments designed to optimize the product for user experience and revenue & promote best practices for multivariate experiments
• Translate complex concepts into implications for the business via excellent communication skills, both verbal and written
• Understand what matters most and prioritize ruthlessly
• Work with cross-functional teams (including Data Science, Marketing, Product, Engineering, Design, User Research, and senior executives) to rapidly execute and iterate

Requirements

• Bachelors’ or above in quantitative discipline: Statistics, Applied Mathematics, Economics, Computer Science, Engineering, or related field
• 3-5 years experience using analytics to drive key business decisions; examples include business/product/marketing analytics, business intelligence, strategy consulting
• Proven track record of being able to work independently and proactively engage with business stakeholders with minimal direction
• Significant experience with SQL and large unstructured datasets such as Hadoop
• Deep understanding of statistical analysis, experimentation design, and common analytical techniques like regression, decision trees
• Solid background in running multivariate experiments to optimize a product or revenue flow
• Strong verbal and written communication skills
• Proficiency in programming/scripting and knowledge of statistical packages like R or Python is a plus

Role Description

We're looking for a Data Scientist to partner with product, engineering, and design teams to answer key questions about how to grow revenue, optimize product, scale and monetize the business, and launch high-impact initiatives. We solve challenging problems and boost business growth through a deep understanding of user behaviors with applied analytics techniques and business insights. An ideal candidate should have robust knowledge of consumer lifecycle and behavior analysis, customer segmentation, digital campaigns, monetization analytics and business operations for a SaaS company.  

Responsibilities

• Develop a deep understanding of customer journey phases and key business metrics
• Perform analytical deep-dives to analyze problems and opportunities, identify the hypothesis and design & execute experiments
• Inform future experimentation design and roadmaps by performing exploratory analysis to understand user engagement behavior and derive insights
• Create personalized segmentation strategies leveraging propensity models to enable targeting of offers and experiences based on user attributes 
• Identify key trends and build automated reporting & executive-facing dashboards to track the progress of acquisition, monetization, and engagement trends. 
• Extract actionable insights through analyzing large, complex, multi-dimensional customer behavior data sets 
• Monitor and analyze a high volume of experiments designed to optimize the product for user experience and revenue & promote best practices for multivariate experiments
• Translate complex concepts into implications for the business via excellent communication skills, both verbal and written
• Understand what matters most and prioritize ruthlessly
• Work with cross-functional teams (including Data Science, Marketing, Product, Engineering, Design, User Research, and senior executives) to rapidly execute and iterate

Requirements

• Bachelors’ or above in quantitative discipline: Statistics, Applied Mathematics, Economics, Computer Science, Engineering, or related field
• 3-5 years experience using analytics to drive key business decisions; examples include business/product/marketing analytics, business intelligence, strategy consulting
• Proven track record of being able to work independently and proactively engage with business stakeholders with minimal direction
• Significant experience with SQL and large unstructured datasets such as Hadoop
• Deep understanding of statistical analysis, experimentation design, and common analytical techniques like regression, decision trees
• Solid background in running multivariate experiments to optimize a product or revenue flow
• Strong verbal and written communication skills
• Proficiency in programming/scripting and knowledge of statistical packages like R or Python is a plus

Role Description

We're looking for a Data Scientist to partner with product, engineering, and design teams to answer key questions about how to grow revenue, optimize product, scale and monetize the business, and launch high-impact initiatives. We solve challenging problems and boost business growth through a deep understanding of user behaviors with applied analytics techniques and business insights. An ideal candidate should have robust knowledge of consumer lifecycle and behavior analysis, customer segmentation, digital campaigns, monetization analytics and business operations for a SaaS company.  

Responsibilities

• Develop a deep understanding of customer journey phases and key business metrics
• Perform analytical deep-dives to analyze problems and opportunities, identify the hypothesis and design & execute experiments
• Inform future experimentation design and roadmaps by performing exploratory analysis to understand user engagement behavior and derive insights
• Create personalized segmentation strategies leveraging propensity models to enable targeting of offers and experiences based on user attributes 
• Identify key trends and build automated reporting & executive-facing dashboards to track the progress of acquisition, monetization, and engagement trends. 
• Extract actionable insights through analyzing large, complex, multi-dimensional customer behavior data sets 
• Monitor and analyze a high volume of experiments designed to optimize the product for user experience and revenue & promote best practices for multivariate experiments
• Translate complex concepts into implications for the business via excellent communication skills, both verbal and written
• Understand what matters most and prioritize ruthlessly
• Work with cross-functional teams (including Data Science, Marketing, Product, Engineering, Design, User Research, and senior executives) to rapidly execute and iterate

Requirements

• Bachelors’ or above in quantitative discipline: Statistics, Applied Mathematics, Economics, Computer Science, Engineering, or related field
• 3-5 years experience using analytics to drive key business decisions; examples include business/product/marketing analytics, business intelligence, strategy consulting
• Proven track record of being able to work independently and proactively engage with business stakeholders with minimal direction
• Significant experience with SQL and large unstructured datasets such as Hadoop
• Deep understanding of statistical analysis, experimentation design, and common analytical techniques like regression, decision trees
• Solid background in running multivariate experiments to optimize a product or revenue flow
• Strong verbal and written communication skills
• Proficiency in programming/scripting and knowledge of statistical packages like R or Python is a plus

Role Description

How many times do you get the opportunity to be on the ground floor of a big and important mission? What if you could be one of the top contributors defining the mission, guiding our teams, and influencing the direction of Dropbox’s AI-first journey? As a Senior Data Scientist for this new division, you will get to do exactly that. You will join a team of top-tier data scientists and become an integral part of the product organization, helping to scale this new business.

Joining on the ground floor of this startup team, you’ll partner directly with Data Science, Product, Engineering and Design leadership to shape the product roadmap, foster a top-tier, data-informed culture, and drive real AI/ML impact and execution along the way!

Responsibilities

• Partner with Product Engineers and Data Engineers to build the reliable, efficient, and scalable data foundations, tools, and processes to drive our AI/ML capabilities’ long-term growth
• Leverage data-driven insights to proactively identify most impactful opportunities, and directly influence product roadmaps and strategies
• Perform exploratory and deep-dive analysis to understand user workflows and engagement patterns on AI features, propose hypothesis, and design & execute experiments with great rigor and efficient data techniques
• Translate complex data insights into implications and recommendations for the business via excellent communication skills, both verbal and written
• Identify what matters most and prioritize ruthlessly for the area you will own
• Contribute to a culture of strong technical ownership, partner with DS leadership to keep evolving DS working model and elevate DS impact
• Work with cross-functional teams (including Product, Engineering, Design, User Research, and senior executives) to rapidly execute and iterate

Requirements

• Bachelors’ or above in quantitative discipline: Statistics, Applied Mathematics, Economics, Computer Science, Engineering, or related field
• 7+ years experience of leveraging data-driven analysis to influence product roadmap and business decision, preferably in a tech company
• Proven track record of being able to work independently, driver measurable business impact, and proactively engage with business stakeholders with minimal direction  
• Proficiency in SQL, Python or other programming/scripting languages
• Deep understanding of statistical analysis, experimentation design, and common analytical techniques like regression, decision trees
• Ability to provide data insights and recommendations for 0→1 product even when sample size is small
• Strong verbal and written communication skills

Role Description

How many times do you get the opportunity to be on the ground floor of a big and important mission? What if you could be one of the top contributors defining the mission, guiding our teams, and influencing the direction of Dropbox’s AI-first journey? As a Senior Data Scientist for this new division, you will get to do exactly that. You will join a team of top-tier data scientists and become an integral part of the product organization, helping to scale this new business.

Joining on the ground floor of this startup team, you’ll partner directly with Data Science, Product, Engineering and Design leadership to shape the product roadmap, foster a top-tier, data-informed culture, and drive real AI/ML impact and execution along the way!

Responsibilities

• Partner with Product Engineers and Data Engineers to build the reliable, efficient, and scalable data foundations, tools, and processes to drive our AI/ML capabilities’ long-term growth
• Leverage data-driven insights to proactively identify most impactful opportunities, and directly influence product roadmaps and strategies
• Perform exploratory and deep-dive analysis to understand user workflows and engagement patterns on AI features, propose hypothesis, and design & execute experiments with great rigor and efficient data techniques
• Translate complex data insights into implications and recommendations for the business via excellent communication skills, both verbal and written
• Identify what matters most and prioritize ruthlessly for the area you will own
• Contribute to a culture of strong technical ownership, partner with DS leadership to keep evolving DS working model and elevate DS impact
• Work with cross-functional teams (including Product, Engineering, Design, User Research, and senior executives) to rapidly execute and iterate

Requirements

• Bachelors’ or above in quantitative discipline: Statistics, Applied Mathematics, Economics, Computer Science, Engineering, or related field
• 7+ years experience of leveraging data-driven analysis to influence product roadmap and business decision, preferably in a tech company
• Proven track record of being able to work independently, driver measurable business impact, and proactively engage with business stakeholders with minimal direction  
• Proficiency in SQL, Python or other programming/scripting languages
• Deep understanding of statistical analysis, experimentation design, and common analytical techniques like regression, decision trees
• Ability to provide data insights and recommendations for 0→1 product even when sample size is small
• Strong verbal and written communication skills

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.

As a Data Scientist on the Mapping team, you will collaborate with our world class team of engineers, product managers, and designers to grow and improve the quality of recommended routes and accuracy of our travel time estimations. We're looking for a passionate, driven Data Scientist who is excited to dive into our spatial data and build a best-in-class mapping product that provides safe, efficient, and seamless navigation for our rideshare drivers.

Data Science is at the heart of Lyft’s products and decision-making. You will leverage data and rigorous, analytical thinking to shape our mapping products and make business decisions that put our customers first. This will involve identifying and scoping opportunities, shaping priorities, recommending technical solutions, designing experiments, and measuring the impact of new features. You will help us solve some of the most impactful problems in mapping, including:

• How do we improve the quality of our map data in order to improve our recommendations?
• How do we benchmark and measure the success of our services?
• How do we validate features of the real world that affect our routing algorithms?
• Are we meeting our travel estimation promises to our customers?

Responsibilities

• Leverage data and analytic frameworks to identify opportunities for growth and efficiency
• Partner with product managers, engineers, and operators to translate analytical insights into decisions and action, and implement products to drive business goals
• Define and implement decision frameworks, measurement strategies, and scientific methodologies that bring consistency and rigor to business decisions and forecasts, balancing opportunity and uncertainty
• Deliver integrated, high-quality analytical outputs spanning multiple projects while navigating ambiguity, cross-team dependencies, and open-ended scope
• Design and analyze online experiments; communicate results and act on launch decisions
• Establish metrics that measure the health of our products, as well as rider and driver experience
• Identify and drive impact and alignment, shaping product and business strategy through data-centric presentations 
• Contribute to the Science community (hiring, onboarding, documentation, knowledge-sharing, tooling improvements), helping make Decision Science at Lyft more effective and scalable

Experiences

• Degree in a quantitative field such as statistics, economics, applied math, operations research or engineering (advanced degrees preferred), or relevant work experience
• 3+ years experience in a data science role or analytics role
• Demonstrated ability to own multi-project analytical scopes with ambiguous problem definitions and cross-functional integration
• End-to-end experience with data, including querying, aggregation, analysis, and visualization
• Proficiency in SQL - able to write structured and efficient queries on large data sets
• Experience in programming, especially with data science and visualization libraries in Python or R, and machine learning libraries such as PyTorch, TensorFlow, Keras
• Experience in online experimentation and statistical analysis, and communicating results and recommendations to senior stakeholders
• Strong communication, critical thinking, and prioritization skills, including the ability to challenge assumptions, propose alternatives, and balance short-term vs. long-term tradeoffs
• Experience in applying machine learning techniques is a plus (e.g. reinforcement learning) to solve customer problems (e.g. personalization, segmentation)
• Expertise in metric design, causal analysis, behavioral analytics, decision frameworks and measurement strategy is a plus
• Experience working with ETL pipelines a plus

Benefits:

• Extended health and dental coverage options, along with life insurance and disability benefits
• Mental health benefits
• Family building benefits
• Child care and pet benefits
• Access to a Lyft funded Health Care Savings Account
• RRSP plan with company match to help save for your future
• In addition to provincial observed holidays, salaried team members are covered under Lyft's flexible paid time off policy. The policy allows team members to take off as much time as they need (with manager approval). Hourly team members get 15 days paid time off, with an additional day for each year of service 
• Lyft is proud to support new parents with 18 weeks of paid time off, designed as a top-up plan to complement provincial programs. Biological, adoptive, and foster parents are all eligible.
• Subsidized commuter benefits and Lyft ride credits

Lyft is committed to creating an inclusive workforce that fosters belonging. Lyft believes that every person has a right to equal employment opportunities without discrimination because of race, ancestry, place of origin, colour, ethnic origin, citizenship, creed, sex, sexual orientation, gender identity, gender expression, age, marital status, family status, disability, pardoned record of offences, or any other basis protected by applicable law or by Company policy. Lyft also strives for a healthy and safe workplace and strictly prohibits harassment of any kind.  Accommodation for persons with disabilities will be provided upon request in accordance with applicable law during the application and hiring process. Please contact your recruiter if you wish to make such a request.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office at least 3 days per week, including on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the Toronto area is $108,000 - $135,000, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

Lyft may use artificial intelligence to screen applicants, however, Lyft employees make the ultimate selection and hiring decisions.

This job fills an existing vacancy.

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.

The Safety & Customer Care (SCC) organization manages over 1.7 million monthly customer interactions and is responsible for ensuring the trust and safety of our riders and drivers. We work every day to build a trusted community, foster safe interactions, manage risky situations, and deliver best-in-class support. As part of this team, you will play a critical role in making transportation safer for all.

We are looking for an experienced and highly motivated Data Scientist to join the Community Safety Data team and lead key initiatives that enhance the safety of Lyft’s community. You’ll work cross-functionally with product management, engineering, design, legal, research, and operations teams to build impactful safety features, models, and systems. This high-impact role requires deep customer empathy, insatiable curiosity to uncover platform risks; and competencies to explore large datasets, develop statistical models, and design data-driven strategies for products that provide safe and engaging experiences for customers.

You’ll report to the Director of Data Science for the SCC org.

Responsibilities:

• Leverage data and analytical frameworks to identify opportunities for improving community trust and safety
• Partner with product managers, engineers, designers, marketers, and operators to translate insights into scalable, safety-forward decisions
• Design and analyze online experiments; interpret and communicate results to influence product launch decisions
• Build and monitor key metrics that reflect rider and driver safety, product health, and policy compliance
• Develop rigorous measurement strategies to evaluate the effectiveness of safety features, interventions, and detection systems
• Drive proactive risk identification and continuous improvement through analysis, experimentation, and iteration
• Mentored junior data scientists by providing guidance, feedback, and support to accelerate their technical and soft skill growth and impact.

Experience:

• Degree in a quantitative field such as Statistics, Economics, Applied Mathematics, Operations Research, or Engineering (advanced degrees preferred), or equivalent professional experience
• 3+ years of industry experience in data science or product analytics roles
• Proficiency in SQL with ability to write efficient queries on large, complex datasets
• Experience programming in Python or R, especially for data analysis, visualization, and modeling
• Strong foundation in statistical analysis and experimentation, including A/B testing design and/or Causal Inference techniques
• Excellent written and verbal communication skills with ability to influence cross-functional partners and executives, and translate data into compelling stories
• Self-starter with a curious mindset and a strong sense of ownership
• Ability to combine business intuition with the application of advanced solutions
• A passion for keeping up with the latest ongoings in Data Science and Machine Learning communities, and an obsession for data quality
• Prior experience in Trust & Safety, or Platform Integrity domains is a plus

Benefits:

• Extended health and dental coverage options, along with life insurance and disability benefits
• Mental health benefits
• Family building benefits
• Child care and pet benefits
• Access to a Lyft funded Health Care Savings Account
• RRSP plan with company match to help save for your future
• In addition to provincial observed holidays, salaried team members are covered under Lyft's flexible paid time off policy. The policy allows team members to take off as much time as they need (with manager approval). Hourly team members get 15 days paid time off, with an additional day for each year of service 
• Lyft is proud to support new parents with 18 weeks of paid time off, designed as a top-up plan to complement provincial programs. Biological, adoptive, and foster parents are all eligible.
• Subsidized commuter benefits and Lyft ride credits

Lyft is committed to creating an inclusive workforce that fosters belonging. Lyft believes that every person has a right to equal employment opportunities without discrimination because of race, ancestry, place of origin, colour, ethnic origin, citizenship, creed, sex, sexual orientation, gender identity, gender expression, age, marital status, family status, disability, pardoned record of offences, or any other basis protected by applicable law or by Company policy. Lyft also strives for a healthy and safe workplace and strictly prohibits harassment of any kind.  Accommodation for persons with disabilities will be provided upon request in accordance with applicable law during the application and hiring process. Please contact your recruiter if you wish to make such a request.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office at least 3 days per week, including on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the Toronto area is CAD $108000-$135,000, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

Lyft may use artificial intelligence to screen applicants, however, Lyft employees make the ultimate selection and hiring decisions.

This job fills an existing vacancy.

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.

Lyft’s Data Science Team builds mathematical models underpinning the platform’s core services. Compared to other technology companies of a similar size, the set of problems that we tackle is incredibly diverse. They cut across optimization, prediction, modeling, inference, transportation, and mapping. We are hiring motivated experts in each of these fields. We're looking for someone who is passionate about solving mathematical problems with data, and are excited about working in a fast-paced, innovative and collegial environment.

You will report into a Science Manager.

Responsibilities:

• Partner with Engineers, Product Managers, and Business Partners to frame problems, both mathematically and within the business context.
• Perform exploratory data analysis to gain a deeper understanding of the problem
• Construct and fit statistical, machine learning, or optimization models
• Write production modeling code; collaborate with Software Engineers to implement algorithms in production
• Design and run both simulated and live traffic experiments
• Analyze experimental and observational data; communicate findings; facilitate launch decisions

Experience:

• Ph.D. in Statistics, Operations Research, Mathematics, Computer Science, or other quantitative fields
• 2+ years professional experience in a technology companies
• Passion for solving unstructured and non-standard mathematical problems
• End-to-end experience with data, including querying, aggregation, analysis, and visualization
• Proficiency with Python, or another interpreted programming language like R or Matlab
• Willingness to collaborate and communicate with others to solve a problem

Benefits:

• Extended health and dental coverage options, along with life insurance and disability benefits
• Mental health benefits
• Family building benefits
• Child care and pet benefits
• Access to a Lyft funded Health Care Savings Account
• RRSP plan with company match to help save for your future
• In addition to provincial observed holidays, salaried team members are covered under Lyft's flexible paid time off policy. The policy allows team members to take off as much time as they need (with manager approval). Hourly team members get 15 days paid time off, with an additional day for each year of service 
• Lyft is proud to support new parents with 18 weeks of paid time off, designed as a top-up plan to complement provincial programs. Biological, adoptive, and foster parents are all eligible.
• Subsidized commuter benefits and Lyft ride credits

Lyft is committed to creating an inclusive workforce that fosters belonging. Lyft believes that every person has a right to equal employment opportunities without discrimination because of race, ancestry, place of origin, colour, ethnic origin, citizenship, creed, sex, sexual orientation, gender identity, gender expression, age, marital status, family status, disability, pardoned record of offences, or any other basis protected by applicable law or by Company policy. Lyft also strives for a healthy and safe workplace and strictly prohibits harassment of any kind.  Accommodation for persons with disabilities will be provided upon request in accordance with applicable law during the application and hiring process. Please contact your recruiter if you wish to make such a request.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office at least 3 days per week, including on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the Toronto area is CAD $108,000 - CAD $135,000, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

Lyft may use artificial intelligence to screen applicants, however, Lyft employees make the ultimate selection and hiring decisions.

This job fills an existing vacancy.

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.

Data Science is at the heart of Lyft’s products and decision-making. You will leverage data and rigorous, analytical thinking to shape our products and make business decisions. This will involve identifying and scoping opportunities, recommending and implementing technical solutions, designing experiments, shaping team priorities, and measuring the impact of new features.

The Verticals team encompasses 3 critical and unique products / marketplaces within Lyft: airports, scheduled rides, and events. Airports are one of the most important, impactful, and complex parts of Lyft’s Rideshare business, as they are a key part of both the rider and driver Lyft experiences and have unique considerations and dynamics. Scheduled rides allow riders to book a ride in advance with high reliability, and enable drivers to add structure to their work schedule. Lastly, events such as concerts or sporting games require similar considerations of special operations to ensure a seamless experience for both riders and drivers. 

As a Data Scientist on the Verticals team, you will collaborate with engineering, product, design, and operations to think critically about the current rider and driver experience and implement product enhancements to facilitate market growth. The ideal candidate can apply strong business acumen to propose product changes, develop end-to-end technical solutions, and is comfortable working with a highly cross functional team. In this role, you will help us tackle problems such as:

• How can we efficiently match riders and drivers together in these unique marketplaces, to minimize ETAs and improve the driver experience? 
• Are we able to forecast ride demand and implement driver guidance to ensure riders can rely on Lyft for an event ride?
• What is the most helpful way to display ride information to drivers so they can make informed decisions?
• What rider segments should we target with ride incentives to grow our marketshare for these verticals? 

Responsibilities:

• Construct and fit statistical or optimization models to make production decisions such as changes to how we match riders and drivers; focusing on model proposal and offline model development 
• Leverage data and analytic frameworks to identify opportunities for growth and efficiency, recommending technical solutions to drive business impact 
• Design and analyze online experiments, including communicating results, acting on launch decisions, and recommending next steps based on learnings
• Partner with product managers, engineers, marketers, designers, and operators to translate data insights into decisions and action
• Develop analytical frameworks to monitor business and product performance, leveraging experiments and causal inference
• Establish metrics that measure the health of our products, the business, as well as the driver experience

Experience 

• Degree in a quantitative field such as statistics, economics, applied math, operations research or engineering (advanced degrees preferred), or relevant work experience
• 3+ years of industry experience in a data science, analytics, or management consulting role
• Proficiency in SQL and Python
• Ability to perform technical experiments, including user split, time split, and region split
• End-to-end experience with data, including querying, aggregation, analysis, and visualization
• Ability to manage, influence, negotiate, and inspire others in a fast-moving environment
• Strong oral and written communication skills, and ability to collaborate with cross-functional partners

Benefits:

• Extended health and dental coverage options, along with life insurance and disability benefits
• Mental health benefits
• Family building benefits
• Child care and pet benefits
• Access to a Lyft funded Health Care Savings Account
• RRSP plan with company match to help save for your future
• In addition to provincial observed holidays, salaried team members are covered under Lyft's flexible paid time off policy. The policy allows team members to take off as much time as they need (with manager approval). Hourly team members get 15 days paid time off, with an additional day for each year of service 
• Lyft is proud to support new parents with 18 weeks of paid time off, designed as a top-up plan to complement provincial programs. Biological, adoptive, and foster parents are all eligible.
• Subsidized commuter benefits and Lyft ride credits

Lyft is committed to creating an inclusive workforce that fosters belonging. Lyft believes that every person has a right to equal employment opportunities without discrimination because of race, ancestry, place of origin, colour, ethnic origin, citizenship, creed, sex, sexual orientation, gender identity, gender expression, age, marital status, family status, disability, pardoned record of offences, or any other basis protected by applicable law or by Company policy. Lyft also strives for a healthy and safe workplace and strictly prohibits harassment of any kind.  Accommodation for persons with disabilities will be provided upon request in accordance with applicable law during the application and hiring process. Please contact your recruiter if you wish to make such a request.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office at least 3 days per week, including on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the Toronto area is CAD $108,000 - CAD $135,000, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

Lyft may use artificial intelligence to screen applicants, however, Lyft employees make the ultimate selection and hiring decisions.

This job fills an existing vacancy.

Upwork Inc.’s (Nasdaq: UPWK) family of companies connects businesses with global, AI-enabled talent across every contingent work type including freelance, fractional, and payrolled. This portfolio includes the Upwork Marketplace, which connects businesses with on-demand access to highly skilled talent across the globe, and Lifted, which provides a purpose-built solution for enterprise organizations to source, contract, manage, and pay talent across the full spectrum of contingent work. From Fortune 100 enterprises to entrepreneurs, businesses rely on Upwork Inc. to find and hire expert talent, leverage AI-powered work solutions, and drive business transformation. With access to professionals spanning more than 10,000 skills across AI & machine learning, software development, sales & marketing, customer support, finance & accounting, and more, the Upwork family of companies enables businesses of all sizes to scale, innovate, and transform their workforces for the age of AI and beyond.

Since its founding, Upwork Inc. has facilitated more than $30 billion in total transactions and services as it fulfills its purpose to create opportunity in every era of work. Learn more about the Upwork Marketplace at Upwork.com and follow us on LinkedIn, Facebook, Instagram, TikTok, and X; and learn more about Lifted at Go-Lifted and follow on LinkedIn.

Sr. Lead AI Research Scientist, AI Evaluation and Reliability

The AI Foundations team leads core research and development across the training, evaluation, and deployment of AI systems that power Uma, Upwork’s flagship AI model, and other customer-facing generative AI capabilities. As a Sr. Lead AI Research Scientist focused on AI Evaluation and Reliability, you will drive high-impact research initiatives that improve the trustworthiness, robustness, and real-world performance of AI systems operating at marketplace scale.

At the Sr. Lead level, this role combines deep technical expertise with cross-functional leadership. You will identify and lead research efforts that address systemic reliability challenges, partner closely with engineering and product teams to translate research into production outcomes, and help shape how Upwork evaluates AI performance in real work scenarios. Your work will support AI systems embedded in retrieval-based workflows, agentic architectures, and human plus AI collaboration patterns, while contributing to Upwork’s broader AI research strategy and external presence.

Responsibilities:

• Lead applied research initiatives focused on AI evaluation, reliability, and robustness, defining success metrics tied to customer impact and production readiness.

• Design and validate methods to measure and mitigate AI reliability risks, including uncertainty estimation, hallucination detection, and identification of model failure modes.

• Partner cross-functionally with engineering, data science, and product teams to integrate research outcomes into customer-facing AI systems and workflows.

• Own research projects end to end, from problem framing and hypothesis development through experimentation, prototyping, and synthesis of results.

• Influence technical direction across teams by surfacing insights, proposing scalable solutions, and aligning stakeholders on priorities and tradeoffs.

• Mentor researchers and engineers through technical guidance, feedback, and collaborative leadership on shared initiatives.

• Contribute to Upwork’s external research footprint through publications, presentations, and engagement with the broader AI research community.

What it takes to catch our eye:

• Proven experience leading applied AI research that balances scientific rigor with real-world deployment constraints and business impact.

• A strong record of research contribution through publications, internal innovation, or demonstrable influence on production AI systems.

• Deep proficiency with Python and modern deep learning frameworks such as PyTorch, with hands-on experience evaluating and improving large-scale models.

• An adaptive approach to integrating AI tools into research and development workflows to accelerate experimentation, improve evaluation quality, and share best practices with others.

• A collaborative, growth-oriented mindset with the ability to mentor peers, communicate complex ideas clearly, and thrive in a fast-evolving, bottom-up environment.
  
  

Come change how the world works.

Upwork is establishing an operational hub in Toronto, Canada. The new office is expected to be fully operational by Q4 2026. This role will require 3 days in office once we have an office open.

This position will initially be employed through a partner to ensure a seamless hiring process while we establish the hub. Once the hub is established, there may be opportunities to transition to employment with Upwork, depending on business needs and other requirements. While employed by the partner, you’ll work as part of Upwork’s team, with access to our resources, culture, and growth opportunities.

Our partner will offer competitive benefits. When Upwork’s hub is established, we will be excited to offer employment and benefits directly as business needs require. 

Upwork is committed to building a diverse, inclusive, and equitable workforce. Employment decisions are made without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, disability, or any other status protected by applicable law.

We use BrightHire, an AI-enabled tool, to record interviews and summarize interview transcripts. The tool allows the interviewer to focus on the discussion and does not score or evaluate candidates or make recommendations. The interview transcripts are reviewed, and decisions are only made by humans. Candidates who prefer not to have their interview recorded through BrightHire can opt out when the interview is scheduled.

To learn more about how Upwork processes and protects your personal information as part of the application process, please review our Global Job Applicant Privacy Notice and the Applicant Privacy Addendum (Canada).

Your role
As an Applied Scientist at Dialpad, you'll be a key driver within our AI team, conducting R&D to power the next generation of autonomous voice agents. While traditional NLP focuses on analyzing static text, your work will center on real-time, multimodal systems that can listen, reason, and take action during live customer interactions. A major focus of the team is advancing DialpadGPT, our proprietary LLM, specifically optimizing it to drive orchestrated agentic workflows and handle complex task execution. Beyond the technical skills, we are a team that values collaboration, continuous learning, and the application of diverse perspectives to solve complex problems. Collaboration will be key as you work alongside our engineering, design, and product teams to build groundbreaking agentic applications.

If you're passionate about agentic and multimodal AI and contributing to a team that's changing the face of business communications, you'll find yourself right at home with us.

This position reports to the Senior Manager of the NLP team and has the opportunity to be based in Vancouver, BC.

What you’ll do

• Research and develop state-of-the-art algorithms for autonomous voice agents, specifically focusing on real-time speech processing and reasoning loops.
• Advance DialpadGPT: Design and execute distributed training strategies to optimize our proprietary LLMs for agentic behaviors, including precise tool use, instruction following, and latency-constrained generation.
• Conduct rigorous evaluation and monitoring of model performances and troubleshoot issues with a keen understanding of resultant business impacts.
• Design and implement orchestration layers that effectively chain LLMs with external tools and APIs to solve complex customer problems autonomously.
• Work with large-scale multimodal datasets (text, audio) to improve model robustness and alignment.
• Collaborate with engineering, product, and design teams to deploy scalable, low-latency models and algorithms in production.
• Submit papers to top-tier academic conferences (ACL, EMNLP, NeurIPS) and contribute to the team’s research culture.

Skills you’ll bring

• Master’s or PhD degree in Computer Science, Machine Learning, Computational Linguistics, or a related quantitative field.
• 2+ years of industry experience in Machine Learning/NLP for Master’s degree holders, or 1+ years for PhD holders.
• Deep understanding of LLMs: Demonstrated experience with training, fine-tuning (PEFT/LoRA), and alignment techniques (RLHF/DPO) for specific domains or tasks.
• Experience with Agentic Systems: Familiarity with building autonomous agents, including concepts like tool use, function calling, reasoning chains (CoT), and memory management.
• Strong proficiency in Python and PyTorch, with the ability to write clean, production-ready research code.
• Research Track Record: A history of publishing in top-tier conferences (ACL, EMNLP, NeurIPS, ICASSP) is highly valued.
• Multimodal Awareness: Familiarity with speech technologies (ASR, TTS) or processing real-time audio streams is a strong plus.
• Ability to bridge the gap between research and product, translating complex technical concepts into business value.
• Familiarity with version control tools like Git for collaborative projects.

Who we are

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.

About the team

Our Data Science team partners deeply with teams across Stripe to ensure that our users, our products, and our business have the models, data products, and insights needed to make decisions and grow responsibly. We’re looking for data scientists with a passion for analyzing data, building machine learning and statistical models, and running experiments to drive impact.  Our work is broad and varied, influencing how our products work (e.g. understanding user needs, preventing fraud, or optimizing charge flows), how our business works (forecasting key outcomes, managing liquidity, quantifying risk exposure), how our go-to-market motions operate (designing growth experiments, optimizing marketing investments, refining sales processes, and estimating causal effects), and everything in between. We have a variety of Data Science roles and teams across Stripe and will seek to align you to the most relevant team based on your background.

What you’ll do

About the internship experience

Our internship program provides the opportunity to work on meaningful business initiatives that will grow the GDP of the internet. Through the internship, you will work with many systems and technologies, gain experience in working with large datasets and analytical methodologies/tools to help us better understand our users and build better products. 

Each intern has a dedicated mentor, and every intern project is part of the team's roadmap that will directly contribute to Stripe's mission. As you collaborate with industry experts on initiatives that expand global commerce, you will develop a strong first-hand understanding of the role analytics plays in steering business strategy and results.

We're not just focused on your immediate contributions; we're invested in your growth. Stripe sees this internship as an opportunity to grow your technical expertise and facilitate personal development, preparing you for a career in the tech industry.

Responsibilities

You will:

• Partner closely with Data Scientists, Data Analysts, and business partners to drive business impact through rigorous analytical solutions
• Apply machine learning, causal inference, or advanced analytics on large datasets to: i) measure results and outcomes, ii) identify causal impact and attribution, iii) predict the future performance of users or products, to drive business success
• Influence business actions and strategy by developing actionable insights through metrics and dashboards.
• Drive the collection of new data and the refinement of existing data sources.
• Learn quickly by asking great questions, finding how to work with your mentor and teammates effectively, and communicating the status of your work clearly
• Present your work to the Data Science team, partner teams, and fellow interns

Who you are

Minimum requirements

We’re looking for someone who has:

• Enrolled in a quantitative PhD program (e.g. Data Science, Statistics, Economics, Mathematics, etc.) with the expectation of graduating in winter 2026 or spring/summer 2027
• Experience with a scientific computing language (such as Python, R, etc) and SQL. We believe new programming languages can be learned if the fundamentals and general knowledge are present!
• Knowledge and hands-on experience in several of the following areas: machine learning, statistics, optimization, product analytics, causal inference, and/or experimentation
• Experience communicating and collaborating with multidisciplinary stakeholders in a team environment

Preferred qualifications

You also likely have:

• Experience writing and debugging data pipelines
• Demonstrated ability to evaluate and receive feedback from mentors, peers, and stakeholders via experience from previous internships or other multi-person projects
• Ability to learn new systems and form an understanding of those systems, through independent research and working with a mentor and subject matter experts

** This role is not eligible for hire in the United States - we are hiring only in Toronto, Ontario, Canada for summer 2026.

What You'll Be Doing:

• Work directly on building, deploying, and iterating on machine learning models and agentic workflow features that address real customer needs
• Apply ML techniques to improve accuracy and overall system performance, ensuring solutions are robust, reliable, and production-ready for customers
• Improve, implement, and validate ML models and agentic workflows supporting submission intake, underwriting decision-making, and automation tasks
• Deploy and adapt autonomous agent behaviors into customer-specific workflows, translating core AI capabilities into practical solutions
• Develop and maintain evaluation pipelines, monitoring systems, and performance metrics to ensure reliability under evolving production conditions
• Monitor production systems via logs, metrics, and user feedback to diagnose issues, debug failures, and drive resolution
• Take end-to-end ownership of problems — implementing fixes or coordinating with engineering and infrastructure teams as needed
• Partner closely with Data Science and Engineering teams to iterate quickly and deliver high-impact solutions

Who We Hope You Are:

• Bachelor's or master’s degree in Mathematics, Operations Research, Data Science, Artificial Intelligence, or a related field with foundational knowledge in machine learning, deep learning, and natural language processing.
• Experience working in a fast-paced, cross-functional environment
• 2+ years of experience as a Machine Learning Engineer, Applied Scientist, or similar role delivering ML solutions in production
• Experience working directly with customers or stakeholders to translate business needs into technical solutions
• Hands-on experience adapting, extending, and deploying ML/LLM systems (including agentic workflows and prompt engineering) in real-world use cases
• Strong experience with experimentation, evaluation, and monitoring pipelines, including analyzing production logs and debugging systems
• Experience deploying and iterating on ML systems in cloud environments in collaboration with engineering teams
• Proven track record of ownership — driving issues through to resolution in production systems

Base Salary Range: $155,000 - $180,000
Final offer amounts are determined by multiple factors including candidate location, experience and expertise and may vary from the amounts listed above. Total compensation package also includes bonus, stock options, benefits and additional perks. 

MaintainX is the world's leading AI-powered maintenance and asset management platform, serving 13,000+ customers including Duracell, Shell, Cintas, and Brenntag. We raised $150M in Series D funding led by Bessemer Venture Partners and Bain Capital Ventures, bringing our total funding to $254M. We were named to the Forbes 2025 Cloud 100, the definitive ranking of the top 100 private cloud companies in the world. We're growing fast and hiring the engineering talent to match.

We're looking for a Senior Software Engineer to own the constraint-solving engine behind our Scheduling Agent, one of the most strategic bets on our Planning & Scheduling roadmap. The engine is a Python service built on CP-SAT that takes work orders, technicians, and a mix of hard and soft constraints, and produces a feasible weekly schedule that enterprise maintenance teams can trust. Your focus will be evolving the solver to handle increasingly complex, real-world scheduling scenarios and exposing it as tools for GenAI agent workflows.

This is a high-ownership role. You'll have the space to shape the modeling approach, partner closely with product and design on what scheduler users actually need, and ship iteratively against feedback from real enterprise customers.

What you'll do

• Own and evolve the Python optimization service that powers the Scheduling Agent, modeling, solving, and iterating on the constraint formulation as new use cases emerge.
• Design and implement increasingly sophisticated scheduling capabilities: trade and crew constraints, irregular capacity patterns, production downtime windows, multi-site considerations, and reactive re-scheduling.
• Build and maintain API routes and tools that expose the solver to GenAI agent workflows (tool calling, structured input/output).
• Partner with PM and design to translate messy real-world scheduling problems into solver constraints, and push back when "optimal" isn't what users actually want.
• Iterate the solver with real users via design partnerships and pilot deployments. Take feedback from human schedulers seriously and reflect it back into the model.
• Contribute to the surrounding Python service: performance, observability, testing, and reliability of the optimization runtime.
• Help shape how scheduling intelligence integrates with the broader MaintainX product over time, including learning from execution data to improve solver inputs.

About you

• 5+ years of professional software engineering experience, with significant time spent on optimization, constraint programming, or operations research problems shipped to real users.
• Strong fluency with CP-SAT and at least one other optimization paradigm (MILP via Gurobi/CPLEX/HiGHS, metaheuristics, or similar). You've hit the limits of one approach and made informed choices about when to use which.
• Solid Python service engineering: APIs, async, testing, profiling, observability. You can own a production service end-to-end.
• Academic grounding in Operations Research, Industrial Engineering, Computer Science, or a related quantitative field, at minimum a strong undergraduate foundation; advanced degrees are common in this space but not required.
• Track record of iterating optimization systems with real users, you've felt what happens when a human rejects the "optimal" answer and you've redesigned the model in response.
• Product mindset and delivery orientation, you ship, you measure, you iterate. You think about the user outcome, not just the objective function.
• Comfort with ambiguity. You can co-design the constraint data model with the team rather than waiting for a clean spec.
• Familiarity with GenAI tooling (LLM tool calling, structured output, prompt design for constrained generation) is expected.

Nice to have

• Experience at a known, reputable product company shipping optimization or scheduling products at scale.
• Exposure to learning-augmented optimization, using historical execution data to estimate durations, priors, or constraint weights.
• Domain experience in scheduling, workforce management, field service, manufacturing, logistics, or similar resource-constrained planning problems.
• Tech-lead experience or interest in growing into a tech-lead role on this team.

What's in it for you

• Competitive salary and meaningful equity opportunities.
• Healthcare, dental, and vision coverage.
• 401(k) / RRSP enrollment program.
• Take what you need PTO.
• A Work Culture where:
• You'll work alongside folks across the globe that reflect the MaintainX values: Smart Humble Optimists.
• We believe in meritocracy, where ideas and effort are publicly celebrated.

About us

Our mission is to deliver one platform for maintenance, repair & operations teams to keep the physical world running. We believe the greatest asset in any organization is the people. That's why we built an intuitive, mobile-first solution to help boost productivity and collaboration across teams and locations.

MaintainX is committed to creating a diverse environment. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.

#LI-Remote

Our co-op/intern experience is unique and designed to prepare you for professional success. Our ultimate goal is to give you the mentorship, training and work experience you need to start your career successfully!

As part of Visiers R&D organization, our data science team is responsible for the development of advanced numerical algorithms and statistical techniques that generate business insights from human resource data. Mentored by an experienced Data Scientist you will collaborate on projects that match both your interests and Visier business needs. This could be software engineering focused (e.g. enhancing our machine learning pipelines or prototyping of automated insight generation), or research focused (e.g. unveiling compelling insights into the workforce from our unique multi-million employee cross-customers data set).

What you'll be doing...

• Modelling the problem at hand, such that it directly solves underlying business problems
• Brainstorming to identify the correct data required to solve business problems
• Performing statistical analysis on data to build statistical/machine learning models
• Identifying the right metrics to measure the success of the models and projects
• Communicating and presenting your work to stakeholders across the organisation
• Conducting independently research, in order to gain domain knowledge and learn cutting-edge techniques

What you'll bring to the table...

• Strong understanding of statistics and how to apply scientific methods to data using algorithms
• Coding skills in Scala, Python or other relevant languages
• Attention to detail and big picture thinking
• Currently pursuing your Masters or PhD
• Knowledge in one of the following areas would be an advantage:
• Hypothesis testing
• Machine learning techniques
• Scientific analysis and knowledge generation

The base pay range for this position in Canada is $5,500k / month.

The compensation offered will be determined by factors such as relevant qualifications, experience, knowledge and skills. Many of our positions are eligible for additional types of compensation (e.g., commission plans, bonus, etc.) which our Talent Acquisition team will share with you if you interview for the role.

We are looking for a Senior Staff Scientist – Fault-Tolerant Quantum Algorithms to help lead the technical development of highly differentiated quantum solutions that will bring value to society. As a Senior Staff Scientist – Fault-Tolerant Quantum Algorithms, you’ll guide a cross-functional team whose mandate is central to IonQ’s mission to solve the world’s most complex problems using the world’s best quantum computers.

As a technical leader in the field of fault-tolerant algorithms, you will help discover the next generation of quantum applications that will exceed state-of-the-art classical methods. You will be a core member of technical leadership in quantum algorithms, and you will drive research into new quantum algorithms and develop IP, computational resource models, long-term roadmaps, and end-to-end workflows that have the potential to disrupt the field of computing. You will also work closely with product, engineering, marketing, sales, operations, and external customers/partners to brainstorm new ideas, propose complex, multi-year initiatives and represent IonQ at high-visibility technical conferences (often all in a single day)!

Responsibilities: 

• Influence the quantum applications roadmap towards fault-tolerant quantum algorithms that will make a large scientific and commercial impact by leading the origination, execution, and optimization of novel, end-to-end quantum algorithms. Guide other team members towards this goal.
• Perform detailed resource analyses of quantum algorithms and perform SWOT analysis of FTQC approaches compared to NISQ and/or classical methods.
• Provide trusted expertise and leadership in FTQC algorithms and communicate the current state-of-the-art, scaling behaviors, trade offs, cross-over points, etc. to stakeholders across the company.
• Lead technical interactions across multi-disciplinary teams to identify, define and solve challenging real-world problems using IonQ trapped-ion quantum computers.
• Define, develop, and drive adoption of industry-leading benchmarking and resource estimation methodologies, metrics, and tools.
• Develop portfolio of foundational IP and high-impact-factor peer-reviewed publications; represent IonQ externally with customer/clients and at conferences.

You’d be a good fit with:

• PhD in physics, mathematics, computer science, or similar field, or comparable field or equivalent work experience + 5 years of professional experience in the quantum computing field
• 13+ years of professional experience or an equivalent combination of education and experience
• Demonstrated expertise in FTQC algorithms that can scale to millions of qubits, billions of gates with line-of-sight to end-customer value: active portfolio of invited talks, repos, licenses, publications and/or patents.
• Active participant in the FTQC algorithm community via, e.g., development of tools (libraries, simulators, compiler), metrics (costing, resource estimation, ROI), and/or community (economic development, standards)
• Relentless dedication to optimizing algorithmic performance (accuracy, time-to-solution) through elegant theoretical equations, as well as engineering optimization
• Mastery of key mathematical techniques used in quantum computing and quantum algorithm development, and a demonstrated ability to communicate scientific results clearly.

You’d be a great fit with: 

• 7+ years of professional work experience developing FT quantum algorithms that align with realistic industry problems such as computer-aided engineering, phase transitions, reaction modeling, cybersecurity, differential equations solving, simulation of physical systems, etc. 
• Deep expertise in quantum algorithms for relevant use cases. This could include quantum simulation (e.g., product formulas, qubitization, qDRIFT, etc.), quantum algorithms and primitives (e.g., block-encodings, LCU, QSP, QSVT, Gibbs sampling, quantum linear systems, etc.), and/or end-to-end fault-tolerant quantum algorithms as applied to detailed use cases.
• Demonstrated expertise in Python and Qiskit or similar quantum SDK; working knowledge of C++.
• Experience proposing, winning, and executing on consultative (paid) contracts; government contracting or defense contracting experience a plus
• Experience commercializing end-to-end software utilizing hybrid computing e.g. CPU + GPU + QPU, Edge, Cloud, etc. Knowledge of CI/CD a plus.

Location: This position can work onsite or hybrid from one of our offices (College Park, MD, Bothell, WA, Toronto, Canada, Gothenburg, Sweden) or fully remote in the US
Travel: 10% domestic and international
Job ID:  1254

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

Why job seekers choose Geotab:

What You Will Do:

• Conduct original research and develop deep distributed reinforcement learning (RL) training systems tailored for the complex gameplay features of Varsapura.
   
   
• Collaborate with cross-functional teams to understand the requirements of gameplay features and ensure machine learning algorithms are effectively integrated into the design.
   
• Take ownership of research project roadmaps, collaborating with engineers to iterate on feature requirements and managing risk for production deployment and maintenance.
   
• Explore and implement advanced techniques in asset processing to ensure high-quality and optimized assets for the game development cycle.
   
• Stay up to date with the latest advancements in machine learning, computer vision, and data compression, applying these technologies to improve the overall gaming experience.

What We Are Looking For:

• MS or PhD degree in Computer Science, Statistics, or a related field, or 8+ years of equivalent high-level research experience.
   
• Strong background in applied machine learning with an expertise on reinforcement learning algorithms 
   
• Hands-on experience designing and developing reinforcement learning tasks from end to end; project experience involving 3D environments, complex action space and spatial reasoning is a strong plus.
   
• Proficiency in programming languages such as Python and C++, and expertise with deep learning frameworks such as PyTorch or TensorFlow.
   
• Experience working in game development with a strong understanding of gameplay systems, and real-time constraints.
   
• Familiarity with distributed training on cloud platforms such as AWS or GCP, including experience debugging performance and managing large-scale experiments.
   
• Solid knowledge of asset processing pipelines, including 3D modeling, texturing, and animation.
   
• Strong problem-solving skills and the ability to work effectively in a collaborative team environment.
   
• Record of publications or contributions to the machine learning and computer vision communities.

Benefits:

The base pay range for this position is $180,000 to $300,000 USD per year. Actual pay is based on location and may vary depending on relevant knowledge, skills, and experience from the candidate. Total compensation may include salary, company bonuses, sign-on bonuses, relocation package, and other benefits such as:

• 100% employer-paid healthcare premiums for you and your dependents
• Generous paid time off
• 401K / RRSP plan
• Employer-paid life and disability insurance
• Team-building activities throughout the year
• Flexible working hours

About impact.com
impact.com is the world’s leading commerce partnership marketing platform, transforming the way businesses grow by enabling them to discover, manage, and scale partnerships across the entire customer journey. From affiliates and influencers to content publishers, brand ambassadors, and customer advocates, impact.com empowers brands to drive trusted, performance-based growth through authentic relationships. Its award-winning products - Performance (affiliate), Creator (influencer), and Advocate (customer referral) - unify every type of partner into one integrated platform. As consumers increasingly rely on recommendations from people and communities they trust, impact.com helps brands show up where it matters most. Today, over 5,000 global brands - including Walmart, Uber, Shopify, Lenovo, L’Oréal, and Fanatics - rely on impact.com to power more than 350,000 partnerships that deliver measurable business results.

Your Role at impact.com:

We're seeking a Senior Data Scientist to serve as an embedded Data Scientist within our Programmatic Experience Group. You'll own the design and deployment of machine learning models that optimize yield, pricing, and inventory allocation at scale — sitting at the intersection of data science, platform engineering, and marketplace economics.

This is a high-craft, high-ownership individual contributor role. You'll work end-to-end: architecting the data pipelines that feed your models, engineering the features that drive performance, and deploying real-time inference systems that make decisions at speed. Your work directly determines how effectively Impact's programmatic marketplace balances advertiser performance with publisher monetization — making this one of the highest-leverage technical roles in the business.

You'll collaborate closely with Product, Data Science, and Programmatic Delivery Engine Engineering, but you operate with significant autonomy. You're expected to bring both the modeling rigor of a data scientist and the production instincts of an ML engineer — and to be genuinely excited about both.

What You'll Do:

Yield Optimization & Pricing Models

• Design and deploy ML models that optimize auction pricing, bid shading, floor price setting, and yield across Impact's programmatic inventory.
• Build and iterate on real-time pricing algorithms that balance short-term revenue efficiency with long-term publisher and advertiser health.
• Develop and maintain feedback loops that allow pricing models to adapt to shifting market conditions, inventory mix, and demand patterns.
• Quantify the revenue impact of pricing model improvements; communicate tradeoffs between yield maximization, fill rate, and partner ROI to stakeholders.

Inventory Allocation & Supply Optimization

• Own ML-driven inventory allocation logic: routing, pacing, and matching supply to demand across partner segments, deal types, and campaign objectives.
• Build models that forecast inventory availability, demand curves, and clearing prices to support proactive allocation decisions.
• Identify and address inefficiencies in inventory utilization — including unsold inventory, suboptimal deal matching, and allocation imbalances across the publisher base.

Data Architecture & Feature Engineering

• Design and own the data infrastructure that feeds programmatic models: event pipelines, feature stores, training datasets, and real-time feature serving.
• Engineer high-signal features from auction logs, bid stream data, user signals, contextual attributes, and historical performance — at the scale of programmatic data volumes.
• Build robust data pipelines with production-grade standards: reliability, observability, versioning, and efficient reprocessing.

Real-Time Inference & Production ML

• Deploy models to production real-time inference environments; own latency, reliability, and throughput requirements for auction-time decision-making.
• Build monitoring systems that track model performance, data drift, and system health in production; define alerting thresholds and retraining triggers.
• Partner with MLOps and Platform Engineering to ensure scalable, low-latency serving infrastructure meets SLOs under high-volume auction traffic.
• Own the full model lifecycle: training, evaluation, deployment, A/B testing, and iteration.

Experimentation & Performance Measurement

• Design and execute rigorous A/B and holdout experiments to measure the causal impact of model changes on yield, fill rate, advertiser performance, and publisher revenue.
• Build evaluation frameworks that go beyond offline metrics — validating model behavior in live auction environments where feedback signals are delayed or noisy.
• Translate experimental results into clear business narratives; present findings and recommendations to Product and business stakeholders.

Self-Learning Systems & Feedback Loops

• Research and implement adaptive, self-learning components within the programmatic stack — including contextual bandits, reinforcement learning signals, and online learning approaches where appropriate.
• Design feedback mechanisms that close the loop between auction outcomes, model updates, and system behavior; reduce reliance on manual tuning and rule-based overrides.
• Stay current with advances in programmatic ML, auction theory, and online optimization; evaluate applicability to Impact's specific marketplace dynamics.

Cross-Functional Collaboration

• Serve as the primary ML technical partner for the Rubicon product and engineering teams; translate business requirements into modeling approaches and communicate technical tradeoffs clearly.
• Collaborate with Data Science peers on shared infrastructure, modeling standards, and cross-domain feature reuse.
• Document models, architectures, and experimental findings to a standard that enables review, replication, and knowledge transfer across teams.

What You Bring:

Required

• Experience: 5+ years in data science, ML engineering, or quantitative research, with at least 2+ years building and deploying ML models in programmatic advertising, ad tech, marketplace optimization, or a closely related domain (e.g., real-time bidding, dynamic pricing, auction systems).
• Programmatic & marketplace depth: Demonstrated understanding of programmatic auction mechanics (RTB, header bidding, floor pricing, deal types, bid shading) and how ML can be applied to optimize outcomes across the supply-demand stack.
• Production ML engineering: Proven ability to take models from prototype to production independently — including real-time inference, monitoring, retraining pipelines, and SLO ownership.
• Data architecture: Experience designing and building data pipelines, feature stores, and training infrastructure for high-volume, low-latency ML systems.
• Technical skills:
• Strong Python and SQL; proficiency with ML libraries (scikit-learn, XGBoost, LightGBM, PyTorch/TensorFlow) and large-scale data tools (Spark, Kafka, or equivalent streaming/batch frameworks).
• Experience with real-time feature serving and low-latency model deployment (REST APIs, gRPC, or streaming inference).
• Familiarity with production ML workflows: model versioning, drift monitoring, A/B testing, evaluation, and retraining.
• Experience processing and modeling at programmatic data scale: high-cardinality auction logs, bid stream data, impression and click events.
• Experimentation rigor: Strong grasp of causal inference and experiment design in online, delayed-feedback environments (auction holdouts, switchback tests, variance reduction techniques).
• Communication: Ability to explain complex modeling decisions and tradeoffs to Product and business stakeholders; comfortable presenting in cross-functional forums.
• Education: Bachelor's in a quantitative field (CS, Statistics, Math, Engineering, Economics, or similar); Master's/PhD preferred.

Preferred / Nice to Have

• Direct experience with SSP, DSP, or exchange-side yield optimization — particularly floor price optimization, bid landscape modeling, or deal matching algorithms.
• Familiarity with auction theory (first-price vs. second-price dynamics, optimal reserve pricing, revenue equivalence) and its practical implications for programmatic ML.
• Experience with contextual bandits, multi-armed bandits, or reinforcement learning applied to real-time decisioning problems.
• Knowledge of online learning and adaptive algorithms in production environments with non-stationary data distributions.
• Familiarity with privacy-preserving ML techniques relevant to programmatic (differential privacy, federated learning, cookieless attribution modeling).
• Experience with GCP tools (BigQuery, Vertex AI, Dataflow, Pub/Sub) and/or Databricks/Spark for large-scale event processing and model training.
• Exposure to supply forecasting, inventory management, or capacity planning in programmatic or marketplace contexts.
• Familiarity with Impact's affiliate and partnership ecosystem, or prior experience at the intersection of performance marketing and programmatic delivery.

What Sets You Apart

• Marketplace intuition. You understand programmatic auctions not just as an engineer but as an economist — you think about incentive structures, equilibrium dynamics, and how model decisions ripple through the supply-demand stack.
• Full-stack ML ownership. You're as comfortable designing a feature store schema as you are tuning a gradient boosting model or debugging a latency spike in production. You own the whole chain.
• Feedback loop thinking. You don't just deploy models — you design the systems that make them smarter over time. You think about how today's decisions become tomorrow's training signal.
• Rigor under real-world constraints. You know how to run clean experiments in environments where feedback is delayed, data is noisy, and business pressures create tradeoffs. You don't let imperfect conditions become an excuse for imprecise thinking.
• Pragmatic delivery. You ship. You balance the perfect with the production-ready, iterate fast, and know when an MVP outperforms a six-month research project.
• Collaborative depth. You build genuine technical trust with engineering and product partners — not just by having good ideas, but by following through, communicating clearly, and making the integration easy.

Salary Range: $165,000 - $185,000 per year, plus an additional 5% variable annual bonus contingent on Company performance and eligible to receive a Restricted Stock Unit (RSU) grant.

*This is the pay range the Company believes is equitable for this position at the time of this posting. Consistent with applicable law, compensation will be determined based on the skills, qualifications, and experience of the applicant along with the requirements of the position, and the Company reserves the right to modify this pay range at any time.

Benefits and Perks: 

At impact.com, we believe that when you’re happy and fulfilled, you do your best work. That’s why we’ve built a benefits package that supports your well-being, growth, and work-life balance.

• Strong extended health benefits: Health & Prescription coverage, vision and dental care, virtual health care, out-of-country medical coverage
• Insurance coverage (life, short-term disability, long-term disability, and more)
• Health Care Spending Account
• Two different Employee Assistance Programs
• Flexible Working: Our Responsible PTO policy means you can take the time off you need to rest and recharge. We're committed to a positive work-life balance and provide a flexible environment that allows you to be happy and fulfilled in both your career and your personal life.
• Health and Wellness: Your well-being is a priority. Our mental health and wellness benefit includes up to 12 fully covered therapy/coaching sessions per year, with additional dependent coverage. We also offer a monthly gym reimbursement policy to support your physical health.
• A Stake in Our Growth: We offer Restricted Stock Units (RSUs) as part of our total compensation, giving you a stake in the company's growth with a 3-year vesting schedule, pending Board approval.
• Investing in Your Growth: We’re committed to your continuous learning. Take advantage of our free Coursera subscription and our PXA courses.
• Parental Support: We offer a generous parental leave policy, 26 weeks of fully paid leave for the primary caregiver and 13 weeks fully paid leave for the secondary caregiver.
• Technology Financial Support: We provide a technology stipend to help you set up your home office and a monthly allowance to cover your internet expenses. 

Note: Benefits may vary by location and are subject to local regulations.

impact.com is proud to be an equal-opportunity workplace. All employees and applicants for employment shall be given fair treatment and equal employment opportunity regardless of their race, ethnicity or ancestry, color or caste, religion or belief, age, sex (including gender identity, gender reassignment, sexual orientation, pregnancy/maternity), national origin, weight, neurodivergence, disability, marital and civil partnership status, caregiving status, veteran status, genetic information, political affiliation, or other prohibited non-merit factors.

#LI_Vancouver_Canada

About the Data Science Organization 

Northbeam’s Data Science organization serves as the intelligence layer of the company, owning the end-to-end science behind marketing measurement and optimization. This includes developing and advancing our core methodologies—Media Mix Modeling (MMM), Incrementality testing, causal multi-touch attribution (MTA), calibration and validation frameworks, and large-scale experimentation systems. The team is also responsible for AI-driven insights, recommendations, and the creation of reusable, scalable, and rigorously validated scientific systems that power Northbeam’s product and customer value.

About the Role

We are seeking a Staff Data Scientist to advance the statistical and experimentation foundations of Northbeam’s marketing intelligence products, including MMM, Incrementality, Insights, and Recommendation engines.  This is a full-stack data scientist role, operating across the entire lifecycle—from researching and developing advanced statistical and causal inference approaches to implementing, deploying, and maintaining production systems.

You will work at the intersection of R&D and product, owning modeling end-to-end: from data exploration and experimental design to model validation, production integration, and live system iteration. You will bring a deep understanding of statistical modeling techniques and, critically, the assumptions underlying them. This role requires strong judgment in selecting, stress-testing, and adapting models to real-world marketing data where ideal conditions rarely exist.

This is a high-agency, hands-on role, requiring comfort shifting between deep technical work, cross-functional collaboration, and customer-facing problem solving as business needs evolve.

Your Impact

• Develop and evolve statistical and causal inference methodologies that strengthen the rigor and reliability of Northbeam’s marketing measurement products.
• Enhance the accuracy, robustness, and scalability of existing MMM pipelines.
• Write production-quality Python and SQL, collaborating closely with Engineering to integrate statistical workflows into scalable systems.
• Rigorously assess model assumptions, identify potential sources of bias, and implement mitigation strategies.
• Debug data and modeling issues in production and improve the reliability of customer-facing results.
• Translate complex statistical reasoning into clear, decision-ready insights for internal stakeholders and customers.

What You Bring

• Bachelor’s degree in Mathematics, Computer Science, or a highly quantitative STEM field (Master’s preferred).
• 6+ years of applied experience in data science, machine learning, or statistical modeling roles.
• Deep applied experience in statistics and causal inference, including hands-on ownership of experiment design (A/B testing, geo-based experiments, or quasi-experimental methods), power analysis, and interpretation of lift.
• Strong understanding of regression, time series modeling, Bayesian methods, and model evaluation in real-world business settings.
• Experience translating statistical research or experimentation frameworks into production-ready systems.
• Proficiency in Python and SQL, with experience working on modern data platforms and statistical modeling libraries.
• Experience working with production code and comfort making changes to live models and pipelines.
• Interest in (or prior experience with) marketing measurement, including MMM and incrementality, with a strong desire to deepen domain expertise.
• Thrives in a small, fast-paced environment, with the ability to drive projects end-to-end.
• Strong communication skills, with comfort explaining technical concepts to non-technical audiences and supporting customer-facing investigations.
• A growth mindset and genuine curiosity about improving measurement quality and decision-making.

Bonus Skills & Experience

• Experience in marketing attribution, MMM, incrementality, or related marketing measurement challenges.
• Experience with optimization techniques (e.g., linear programming, mixed-integer programming) applied in production systems.
• Familiarity with MLOps practices and data pipeline orchestration tools.
• Startup Experiences.
• PhD in a quantitative field. 

Why Northbeam

Unlike most Series A+ companies, we have a proven business model, strong revenue, and a growing waitlist of customers eager for the products we’re building. We are in a high growth phase and we are nearly profitable.

We sit at the center of the performance-marketing ecosystem with deep partnerships across major ad platforms (Meta, Snap, TikTok, AppLovin, and more). Our unique combination of high-quality partner integrations, rich 1st-party data, and a robust identity graph gives us an advantage that is extremely difficult for competitors to replicate.

Northbeam is where top-tier data talent can directly shape the industry’s most important measurement challenges at scale.

#LI-Remote

What do we need

We are seeking a Scientific Technical Solutions Lead to join the team developing scientific Solutions built on Dotmatics LUMA, a next-generation, multi-tenant, AI-powered data management and workflow automation for multimodal scientific discovery. As a Scientific technical lead on the team, you will work closely with other Solution Architects, product management, engineering teams, and external stakeholders to deliver exceptional product capabilities via Luma Solutions.

Your focus will be on designing and implementing the configuration necessary to realise product vision and on ensuring that this configuration follows best practices within the Life Sciences and Chemical & Material space.

This role offers the opportunity to shape a foundational part of the modern lab—helping scientists work faster, smarter, and with greater impact.

In this role you will get to:

• Design configuration: Collaborate with Product to determine application structure, define entity/data models, specify task workflows (Scientific Workflows) , and define desired data visualization experiences
• Develop configuration: Create registration entities, data flows, task templates, data visualization experiences, and other configurations in support of developing Luma Solutions that will be delivered as part of the product
• Test and troubleshoot configuration: Perform in-team testing of configuration and assist is handing test cases to the QA group for release testing. Troubleshoot configuration to understand why it is not producing desired results.
• Contribute to documentation: Write draft documentation of configuration and workflows and collaborate with the Documentation team to develop final customer-facing documentation
• Train internal and external users: Train members of other Dotmatics teams and early customers on how to use Luma Solutions configuration and the workflows it supports
• Provide input into new product features: Use new product features in Luma Solutions and provide feedback on the experience, participate in user experience testing, and provide input to the Product team about features that will enable the best Solutions
• Champion quality and best practices: Actively participate in the development of best practices for Luma configuration, and ensure these best practices are followed in the development of Luma Solutions

We are looking for people who have 10+ years of hands-on experience building and delivering informatics solutions for Life Sciences customers. The ideal candidate holds a Bachelor's or advanced degree in Life Sciences, Data Science, Chemistry, Biology, Bioinformatics, or a related technical discipline — and brings a genuine passion for quality and continuous improvement, fueled by a drive to solve problems that truly matter.

The key skills we are looking for: 

• Life Science Industry experience, either as a Scientific Solution Architect, technical application Scientist or a Scientist who has been heavily involved with designing products or technical applications
• Advanced expertise in data modeling, with a proven ability to design robust, scalable data structures
• Expert-level proficiency in configuring low-code platforms and designing end-to-end workflows
• Demonstrated success delivering scientific data platforms or tools spanning data modeling, ETL, APIs, and BI/reporting
• Expert-level SQL skills, with strong working knowledge of Python and JSON
• Deep experience integrating tools and modules to architect complete, cohesive solutions
• Strong analytical ability to translate product requirements into precise technical configuration designs
• Excellent communication skills — able to distill complex technical concepts for both technical and non-technical audiences at all levels
• Experience running and/or supporting scientific workflows in a production or research environment

You may also have 

• Experience running scientific test cases
• Understanding of software testing and or SDLC
• Working knowledge of Databricks
• Drug Discovery or Biotechnology industries or Other scientific Industries

Overview

This is a general posting for multiple Senior Data Science roles open across our 4-sided marketplace. Roles are open at both the L5 (Senior Data Scientist I) and L6 (Senior Data Scientist II) levels. You’ll get the chance to learn about the different problems the Data Science team solves as you go through the process. Towards the end of your process, we’ll do a team-matching exercise to determine which of the open roles/teams you’ll join. At the bottom of this posting, you’ll find a breakdown of the currently open roles.

About the Job

• Own analytical frameworks that guide the product roadmap.
• Design rigorous experiments and interpret results to draw detailed and actionable conclusions.
• Develop statistical models to extract trends, measure results, and predict future performance of our products.
• Build simulations to project the impact of various product and policy interventions.
• Enable objective decision-making across the company by democratizing data through dashboards and other analytical tools.
• Use expertise in causal inference, machine learning, complex systems modeling, behavioral decision theory, etc., to shape the future of Instacart.
• Present findings in a compelling way to influence Instacart’s leadership.

About You

Minimum Qualifications

• 5+ years of experience working in a quantitative role at a product company or a research organization.
• Ability to run rigorous experiments and generate scientifically sound recommendations.
• Ability to write complex, efficient, and eloquent SQL queries to extract data.
• Ability to write efficient and eloquent code in Python or R.
• A desire to build and improve consumer software products.
• Ability to translate business needs into analytical frameworks.
• Eagerness to learn, flexibility to pivot when needed, savviness to navigate a dynamic environment, and a growth mindset to build a successful team and company.

Preferred Qualifications

• Awareness of business trade-offs when working on a multi-sided marketplace.
• Confidence in collaborating with and influencing cross-functional stakeholders (e.g., Product, Engineering) at a senior level.
• MS/PhD in Statistics, Economics, Applied Mathematics, or a related field.

Currently Opened Roles

Ads Auctions | Sr. Data Scientist I (L5)

The Ads Auctions team is responsible for innovating and enhancing auction mechanisms to create a competitive and transparent marketplace that benefits both advertisers and consumers. In this role, you’ll own analytical frameworks and simulations that shape the evolution of our CPC pricing, bid optimization, and auction selection processes—running rigorous experiments to evaluate mechanism changes such as bid shading and dynamic pricing. You’ll partner closely with Engineering, ML, and Product to translate business needs into well-specified model requirements, and apply expertise in auction theory, mechanism design, and econometrics to ensure our systems are fair, efficient, and aligned with marketplace goals.

AI for Analytics | Sr. Data Scientist II (L6)

Instacart is developing AI-powered systems to accelerate analytical work and enable self-serve analytics at scale, with a goal of 2x-ing the number of features launched by end of 2026. As a Senior Data Scientist II, you’ll own product strategy and AI expertise—identifying high-impact opportunity areas and deploying AI and ML systems in production settings. This is a senior cross-functional leadership role: you’ll shape the product roadmap, ruthlessly prioritize across competing stakeholders, and set the analytical direction for how Instacart’s data experts and AI systems work together.

Order Experiences (Search) | Sr. Data Scientist I (L5)

The Order Experiences (Search) team is focused on making it fast and effortless for customers to find the right items and complete their order with confidence. As a Senior Data Scientist, you’ll own the analytics and experimentation strategy that powers how we interpret customer intent and surface the most relevant results—driving search conversion, order rate, and GTV. You’ll partner closely with Product, Engineering, and ML to shape the roadmap for search relevance and ranking quality, running experiments across retrieval, ranking, and search UX while connecting offline model evaluation to real-world business outcomes.

Ecosystems | Sr. Data Scientist II (L6)

The Ecosystems team tackles some of Instacart’s most strategic macro-level analytical challenges, including quantifying the impact of economic trends, competitive dynamics, and seasonality on core business metrics. As a Senior Data Scientist II, you’ll own time-series forecasting for key business outcomes, develop measurement frameworks for evaluating competing growth initiatives, and build AI tooling to scale analytics workflows across the team. This role also carries a technical leadership dimension—you’ll set the methodological bar, mentor peers, and review analyses to drive rigor across the organization.

Delivery | Sr. Data Scientist II (L6)

The Delivery team is dedicated to ensuring customers receive their complete orders seamlessly, spanning the full flow from capturing customer information to coordinating the shopper experience. As a Senior Data Scientist II, you’ll identify strategic opportunities to enhance the delivery experience, design and execute rigorous experiments to evaluate new product features, and leverage geospatial data and advanced analytical techniques to optimize logistics. You’ll also proactively identify high-risk delivery scenarios and develop solutions to the underlying systemic issues that drive them.

Marketing | Sr. Data Scientist I (L5)

The Marketing Data Science team is responsible for measurement and optimization across Instacart’s paid digital channels, including search, shopping, display, social, and video. As a Senior Data Scientist, you’ll build and maintain sophisticated attribution models—including multi-touch and incrementality-based approaches—and develop scalable data pipelines to turn raw marketing data into clean, analysis-ready datasets. You’ll apply causal inference methods such as geo holdouts and difference-in-differences to evaluate campaign effectiveness, partnering closely with marketing, data engineering, and business teams to define measurement frameworks and drive data-informed decisions. You'll also model user lifetime value (LTV) across acquisition cohorts, quantifying how marketing spend and channel mix influence long-term retention and monetization to inform budget allocation and growth strategy

#LI-Remote

About The Team 

Cerebras builds wafer-scale AI processors—single chips delivering tens of PB/s of memory bandwidth and a dataflow architecture that accelerates at a granularity no multi-device system can match. The Advanced Technology Group (ATG) is Cerebras’ pathfinding organization. We work ahead of product to explore new architectures, demonstrate breakthrough performance on scientific and AI workloads, and shape the technical roadmap for future Cerebras hardware and software. Our work regularly appears at top-tier venues (Supercomputing, SIAM, IEEE, and NeurIPS) and directly influences the design of next-generation wafer-scale systems. 

About The Role 

Most AI research today is shaped by the constraints of existing hardware. This role starts from the other direction: what would you build if the architecture let you rethink the fundamentals? You will design and develop AI models and training methodologies on wafer-scale hardware, working at the level of optimization theory, model architecture, and statistical foundations rather than assembling existing components. 

The ATG sits at the intersection of AI, computational science, and computer architecture, and your work will draw on all three. You will collaborate closely with Cerebras’ ASIC, compiler, kernel, and AI teams as well as external partners at universities and national laboratories. 

What You Will Do 

• Design AI models and training methods from first principles, leveraging architectural properties of wafer-scale hardware that are unavailable on conventional platforms. 
• Investigate how techniques from computational science—numerical methods, PDE solvers, simulation—can inform and advance AI model design, and explore hybrid workflows that couple simulation and learning. 
• Develop a deep understanding of the hardware substrate and use it to guide algorithmic choices: model structure, optimization strategy, memory access patterns, numerical precision. 
• Publish findings and present at top-tier venues (NeurIPS, ICML, ICLR, etc.); represent Cerebras in the broader AI/ML research community. 
• Inform the design of future Cerebras hardware and software by identifying the computational patterns that matter most for next-generation AI workloads. 

What We Are Looking For 

• PhD in Machine Learning, Computer Science, Applied Mathematics, Statistics, Physics, or a related quantitative field preferred; exceptional candidates without a graduate degree who demonstrate equivalent depth through published research, significant open-source contributions, or a strong industry track record are encouraged to apply. 
• Mathematical maturity: comfort with the theory behind gradient methods, loss landscapes, generalization, and the relationship between model structure and data statistics. 
• Track record of published research at top-tier AI or computational science venues. 
• Proficiency in Python and PyTorch; comfort with C or other low-level languages is a strong signal. 
• Excellent communication and interpersonal skills: able to present complex technical material to both ML and systems audiences, and to collaborate effectively in a fast-paced, small-team environment. 

Why This Opportunity Is Exciting And Unique 

• You will have direct access to hardware that changes what’s algorithmically possible. Tens of PB/s of memory bandwidth and fine-grained dataflow execution open design spaces that don’t exist on GPU clusters. 
• You will work alongside researchers in computational science, computer architecture, and performance engineering. The synthesis across these fields is central to ATG’s approach. 
• Your research will influence silicon - ATG’s findings directly shape the design of future Cerebras chips and systems. 

We are hiring for multiple positions across experience levels. If this work resonates, we encourage you to apply. 

About The Role

As an Applied Machine Learning Research Scientist at Cerebras, you will play a key role in turning modern machine learning techniques into scalable, high-performance systems. This role sits at the intersection of modeling and systems focused not on publishing new algorithms, but on understanding how they work and making them run effectively at scale. Your work will directly impact how large language models (LLMs) are trained, optimized, and deployed on one of the most advanced AI platforms in the world. 

You will work closely with researchers and senior engineers to implement and improve workflows for LLM pretraining, fine-tuning, and reinforcement learning-based post-training. This includes building training pipelines, debugging complex system behaviors, improving model quality, and iterating on data and evaluation strategies. Your contributions will help translate cutting-edge ML ideas into reliable, production-ready systems that solve real-world problems. 

This role is ideal for candidates who enjoy hands-on engineering, want to build deep intuition for ML systems, and are excited about working on LLMs and reinforcement learning in practice, not just in theory. 

Responsibilities 

• Apply post-training techniques (e.g. RLVR, RLHF, GRPO etc.) techniques to improve model performance.
• Build and maintain evaluation pipelines to measure model performance across tasks and domains.
• Debug issues across the ML stack, including data pipelines, training jobs, model outputs and mixed or lower precision computation.
• Collaborate with researchers to translate ML ideas into efficient, scalable implementation.
• Design, implement, and scale ML pipelines across all stages of LLM development (pretraining, fine-tuning, alignment).
• Work with large datasets, including dataset generation, filtering, and synthetic data approaches.
• Optimize training and inference workflows for performance, efficiency, and reliability.
• Contribute high-quality, maintainable code to shared ML infrastructure. 

Skills & Qualifications    

•  Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 0 - 5 years of experience (including internships, research, or industry experience) working with machine learning systems; we are hiring multiple positions for various levels. 
• Strong programming skills in Python.
• Experience with ML frameworks such as PyTorch.
• Solid understanding of machine learning fundamentals.
• Familiarity with deep learning architectures, particularly transformers.
• Ability to read and understand modern ML papers and implement key ideas.

Preferred Skills & Qualifications 

• Experience working with large language models (training, fine-tuning, and evaluation).
• Familiarity with reinforcement learning concepts.
• Experience with distributed training frameworks (e.g., FSDP, Megatron).
• Experience working with large-scale datasets and data pipelines.
• Experience debugging or optimizing ML systems for performance.
  • Contributions to meaningful codebases, projects, or open-source systems 

Are you driven by data, inspired by innovation, and eager to shape the future of insurance?

Join a fast-growing Canadian P&C insurer where you'll play a pivotal role in advancing our Actuarial Services function. We're building on a solid foundation—powered by cutting-edge technology, a robust enterprise data analytics team, and bold investments in AI. With this groundwork in place our talented team is now focused on scaling and refining our capabilities.

We're looking for bright, analytical minds to help bring our strategy to life. If you're passionate about solving complex problems and want to explore Pricing, Underwriting, and Claims, we want to hear from you.

Senior Data Scientist

We're looking for a Senior Data Scientist to join our growing team and build the next generation of predictive analytics solutions. In this role, you won’t just be crunching numbers, you’ll be using machine learning (ML) and artificial intelligence (AI) models to transform raw data into powerful insights that drive our business forward.

This is an opportunity to directly contribute to our core operations, working alongside a supportive team who are invested in your growth. If you have a passion for data and a desire to see your work make a tangible impact, this role is for you.

What you'll do:

• Deliver value through data-driven algorithms and models: You will apply the right mix of ML and AI to solve high-impact business problems. You’ll partner with stakeholders to select and defend the best model architecture and explainability approaches to balance performance, interpretability, and trade-offs.
• Embed ML/AI into core business capabilities: You will build deep expertise in P&C insurance workflows and translate that knowledge into practical ML/AI-powered analyses, decision tools, and visualizations that improve how work gets done.
• Partner with stakeholders to ensure adoption, value, and model integrity: You’ll align with stakeholders on goals and success metrics, then pressure-test solutions against real constraints to ensure they’re feasible and valuable. You will communicate your insights and recommendations with clarity and confidence to both technical and non-technical audiences.
• Invest in Your Future: We're committed to your professional development. You'll engage in project-specific research to continually grow your skills and have the unique opportunity to collaborate with our Claims and Finance departments.

 What You’ll Need to Succeed

• Education: You have a master’s degree in a quantitative or technical discipline
• Experience: You have 3–5 years of experience in an ML/AI development role, with experience deploying ML / AI models into production and associated governance procedures.
• Technical Skills: You have a broad understanding of modern ML architectures, familiarity with cloud and ML platforms. You have strong Python programming skills and experience with various ML libraries.
• Core Competencies: You have excellent analytical, quantitative, and critical thinking skills. You are highly analytical and use your communication and collaboration skills to engage with stakeholders to solve problems.

If you're ready to be a core part of our mission and make a tangible impact from day one, we want to hear from you.

#LI-Hybrid

The expected base salary range for this position is $79,500 - $129,500. Depending on your relevant experience, skills, qualifications, market conditions and business needs, base compensation may vary. You have the potential to earn more through Gore Mutual’s discretionary bonus program which gives you an opportunity to increase your total compensation, provided the business meets its performance targets and you meet your individual goals.

Please note: This range reflects the expected base salary for this role but may not represent the full compensation range for all experience and skill levels. During the recruitment process, we will discuss and consider how your unique qualifications align with the broader range for this position.

Gore Mutual is proud to offer a comprehensive total rewards package which includes extended health and dental benefits, disability insurance, retirement plan matching, paid time off, recognition and perk programs.

Shape the future of enterprise AI technology

Do you want to turn cutting-edge natural language processing into products impacting millions of users?

As a Senior II Applied Scientist on Coveo’s Knowledge AI team, you will help build the language technologies at the core of our generative artificial intelligence platform.

You’ll take on a technical leadership role, influencing our NLP vision while designing, evaluating, and deploying advanced solutions such as retrieval augmented generation pipelines, information retrieval, semantic search, question answering, and agentic systems, always with real-world constraints in mind.

As one of our Senior II Applied Scientists, you will:

• Design, develop, and optimize modern language-based artificial intelligence solutions, from data and modeling choices to evaluation strategies and production constraints.
• Apply and serve transformer-based language models at scale, delivering reliable, real-time experiences to enterprise users.
• Collaborate closely with machine learning developers and the machine learning platform team to accelerate research and innovation cycles.
• Act as a technical reference within the team, guiding architectural decisions and promoting best practices in applied science.
• Mentor applied scientists, supporting their growth and helping raise the overall level of expertise within the group.
• Contribute to research and development processes that foster efficiency, rigor, and a culture of applied science excellence.
• Share our innovations externally through technical blog posts, presentations, or other thought leadership content.

Here is what will qualify you for the role:

• A PhD, or a Master’s degree with equivalent research experience, in a relevant field such as machine learning, computer science, mathematics, or physics.
• At least 8 years of industry experience applying modern natural language processing techniques, including deep learning and large language models, to real-world systems at scale.
• Strong experience designing models and systems that balance performance, user experience, and production realities.
• Ability to deliver production-grade code that is well-tested, maintainable, and evaluated through rigorous experimentation.

What will make you stand out:

• Experience in a technical leadership capacity, including mentoring, hiring, roadmap definition, or scientific project planning.
• Research or applied experience in areas closely related to Coveo’s business, such as information retrieval, neural search, semantic search, or question answering.
• Exposure to more traditional lexical or linguistics-based natural language processing techniques.
• International work experience and a demonstrated ability to collaborate across cultures and perspectives.

Do you think you can bring this role to life? Or add your own color?
You don’t need to check every single box; passion goes a long way and we appreciate that skillsets are transferable.

Send us your application, we want to hear from you!

Join the Coveolife!

We encourage all qualified candidates to apply regardless of, for example, age, gender, disability, gaps in CV, national or ethnic background.
Coveo is committed to providing accessible employment practices. If you require accommodation due to a disability at any point during the recruitment process, please contact HR@Coveo.com to discuss your needs.

Shape the future of enterprise AI technology

Do you want to turn cutting-edge natural language processing into products impacting millions of users?

As a Senior II Applied Scientist on Coveo’s Knowledge AI team, you will help build the language technologies at the core of our generative artificial intelligence platform.

You’ll take on a technical leadership role, influencing our NLP vision while designing, evaluating, and deploying advanced solutions such as retrieval augmented generation pipelines, information retrieval, semantic search, question answering, and agentic systems, always with real-world constraints in mind.

As one of our Senior II Applied Scientists, you will:

• Design, develop, and optimize modern language-based artificial intelligence solutions, from data and modeling choices to evaluation strategies and production constraints.
• Apply and serve transformer-based language models at scale, delivering reliable, real-time experiences to enterprise users.
• Collaborate closely with machine learning developers and the machine learning platform team to accelerate research and innovation cycles.
• Act as a technical reference within the team, guiding architectural decisions and promoting best practices in applied science.
• Mentor applied scientists, supporting their growth and helping raise the overall level of expertise within the group.
• Contribute to research and development processes that foster efficiency, rigor, and a culture of applied science excellence.
• Share our innovations externally through technical blog posts, presentations, or other thought leadership content.

Here is what will qualify you for the role:

• A PhD, or a Master’s degree with equivalent research experience, in a relevant field such as machine learning, computer science, mathematics, or physics.
• At least 8 years of industry experience applying modern natural language processing techniques, including deep learning and large language models, to real-world systems at scale.
• Strong experience designing models and systems that balance performance, user experience, and production realities.
• Ability to deliver production-grade code that is well-tested, maintainable, and evaluated through rigorous experimentation.

What will make you stand out:

• Experience in a technical leadership capacity, including mentoring, hiring, roadmap definition, or scientific project planning.
• Research or applied experience in areas closely related to Coveo’s business, such as information retrieval, neural search, semantic search, or question answering.
• Exposure to more traditional lexical or linguistics-based natural language processing techniques.
• International work experience and a demonstrated ability to collaborate across cultures and perspectives.

Do you think you can bring this role to life? Or add your own color?
You don’t need to check every single box; passion goes a long way and we appreciate that skillsets are transferable.

Send us your application, we want to hear from you!

Join the Coveolife!

We encourage all qualified candidates to apply regardless of, for example, age, gender, disability, gaps in CV, national or ethnic background.
Coveo is committed to providing accessible employment practices. If you require accommodation due to a disability at any point during the recruitment process, please contact HR@Coveo.com to discuss your needs.

PLEASE NOTE: This is a fully remote, 1099 independent contractor opportunity with an estimated duration of six months and the potential for extension. To be eligible, candidates must be authorized to work in Canada.

About the Program

The Human Frontier Collective (HFC) Fellowship brings together top researchers and domain experts to collaborate on high-impact work that are shaping the future of AI. As an HFC Fellow, you’ll apply your academic and professional expertise to help design, evaluate, and interpret advanced generative AI systems—while gaining exposure to cutting-edge research and working alongside an interdisciplinary network of leading thinkers.

What You'll Do

• ML Projects: Get invited to engage in high-impact projects with our partnered AI labs and platforms. Help models understand real-world deep learning workflows by designing, reviewing, and optimizing PyTorch models, evaluating complex ML code and AI-generated implementations for efficiency and correctness, and advising on GPU optimization, scaling, and trade-offs.
• HFC Community: Beyond the work, you’ll become part of a supportive, interdisciplinary network of innovators and thought leaders committed to advancing frontier AI across domains.
• Contribute to Research Publications: Collaborate with Scale’s research team to co-author technical reports and research papers—boosting your academic visibility and professional recognition (e.g., SciPredict, PropensityBench, Professional Reasoning Benchmark).

Who Should Apply

• Education: PhD or postdoctoral degree in Computer Science, Computer Engineering, or a related field.
• Professional Background: 1-3+ years of experience as a Machine Learning Engineer or Data Scientist.
• Skills: Strong proficiency in Python and modern ML frameworks (PyTorch, TensorFlow). Experience with cloud infrastructure (AWS) and MLOps tools (Docker, Langchain) is a plus.
• Professional Mindset: Detail-oriented, innovative thinker with a passion in applied AI research and a commitment to collaboration.

Why Join the HFC?

• Professional Development: High-impact experts expand their influence through review projects, advisory roles, and research, while deepening their AI expertise, strengthening analytical and problem-solving skills, and engaging with pioneering AI applications in science and technology.
• Join a Top-Tier Network: Collaborate with a global network of engineers and experts to advance responsible AI through impactful, flexible research and training. 80% of our members come from leading institutions.
• Flexible Schedule: Set your own schedule, with flexible 10–40 hour weeks that fit around your life and other commitments.
• Competitive Pay: Project pay rates vary across platforms and are depending on a number of factors, including but not limited to; projects, scope, skillset, and location. 

Application Process

1. Apply: We review applications on a rolling basis.
2. Interview: Candidates will get to discuss their research experience, professional background, and alignment with our mission to advance human-centered AI.
3. Join the Collective: Successful candidates will receive an invitation to join the Human Frontier Collective Fellowship.

Overview

The Ads Auctions team is responsible for innovating and enhancing auction mechanisms to create a competitive and transparent marketplace that benefits both advertisers and consumers. We focus on the full auction workflow—candidate filtering, pricing strategies, bid optimization, and selection processes—with the goal of balancing user experience with advertiser value. As a Senior Data Scientist on this team, you will be a core technical voice shaping how our auction systems evolve, driving improvements to our CPC (cost-per-click) infrastructure and bidding algorithms that directly impact advertiser performance and Instacart's ads revenue.

About the Job

• Design and own analytical frameworks that guide the evolution of our auction and bidding systems, including CPC pricing, bid landscape modeling, and reserve price optimization.
• Run rigorous experiments to evaluate the impact of auction mechanism changes—such as bid shading, second-price vs. generalized second-price auctions, and dynamic pricing—and translate results into actionable recommendations.
• Develop statistical and machine learning models to forecast bid distributions, predict click-through rates, and optimize auction efficiency across advertiser and consumer dimensions.
• Build simulations to model auction dynamics and stress-test proposed mechanism changes before live deployment.
• Partner closely with Engineering, ML, and Product to translate business and advertiser needs into well-specified model and system requirements.
• Enable objective decision-making by building dashboards and monitoring frameworks that surface auction health, advertiser performance, and CPC trends to stakeholders across the organization.
• Apply expertise in causal inference, mechanism design, and econometrics to ensure our auction systems are fair, efficient, and aligned with marketplace goals.
• Present analytical findings and strategic recommendations in a compelling way to influence Instacart's leadership and Ads go-to-market teams.

About You

Minimum Qualifications

• 5+ years of experience working in a quantitative role at a product company or research organization, with meaningful exposure to ads, auctions, or marketplace pricing systems.
• Hands-on experience with auction mechanics, CPC or CPM bidding systems, bid optimization, or related ad monetization problems.
• Ability to run rigorous experiments and generate scientifically sound recommendations in the context of auction or pricing changes.
• Ability to write complex, efficient, and eloquent SQL queries to extract data.
• Ability to write efficient and eloquent code in Python or R.
• A desire to build and improve consumer software products and advertising platforms.
• Ability to translate business needs—from both advertiser and marketplace perspectives—into analytical frameworks.
• Eagerness to learn, flexibility to pivot when needed, savviness to navigate a dynamic environment, and a growth mindset to build a successful team and company.

Preferred Qualifications

• Deep familiarity with auction theory, mechanism design, or algorithmic pricing (e.g., generalized second-price auctions, VCG, bid shading, reserve pricing).
• Experience building or analyzing CPC/CPM bidding systems, budget pacing, or bid landscape models at scale.
• Awareness of business trade-offs when working on a multi-sided marketplace, particularly the interplay between advertiser ROI, consumer experience, and platform revenue.
• Confidence in collaborating with and influencing cross-functional stakeholders (e.g., Product, Engineering, Sales) at a senior level.
• MS/PhD in Statistics, Economics, Operations Research, Applied Mathematics, Computer Science, or a related field.

#LI-Remote

The Role

This is a full-ownership data engineering role at the center of Medeloop's AI platform. You won't be maintaining pipelines someone else built,  you'll be architecting the data backbone that powers AI agents doing real operations at scale. You'll work directly with data scientists, AI engineers, and product teams to turn raw, complex healthcare data into the clean, structured, semantically-rich foundation our AI scientists depend on. Your work shows up in customer products and research outcomes, not internal dashboards that no one reads.Candidates who currently perform these tasks exclusively through manual processes are unlikely to be suitable for this role. We require an individual who has already adopted and integrated AI techniques to enhance operational velocity, rather than one who is contemplating future experimentation.If you want to build something that genuinely changes how medical research gets done, this is the role.

What You'll Own

• The healthcare data lake: curating, extending, and evolving it through new concepts, derived variables, and data models that directly inform our AI engines and customer products
• AI-native data workflows: designing and operating AI-powered pipelines (using tools like Claude Code and agent frameworks) to automate harmonization, cleaning, quality checks, and summarization at scale
• NLP and semantic infrastructure: building pipelines for entity extraction, concept normalization, embedding-based retrieval, and semantic search that power the AI Scientist platform
• Novel data extraction approaches: experimenting with and building new methodologies for working with unstructured clinical data, not just applying existing playbooks
• Research-grade data products: delivering analytical samples, cohorts, and final datasets that withstand scientific scrutiny and are actively used by researchers and customers
• Data governance and observability protocols: including access controls, PHI/PII handling, data classification, compliance, monitoring, alerting, data freshness, and comprehensive documentation to enable self-service capabilities.

What We're Looking For

• 3+ years of relevant data engineering or data management within an analytics-driven organization, with end-to-end ownership from raw ingestion to final data product
• Deep hands-on experience with healthcare CDMs (OMOP, FHIR, PCORnet) — designing or extending them, not just querying
• Knowledge of medical ontologies: UMLS, SNOMED CT, RxNorm
• Experience with big data, data pipelines and tooling that support retrieval-augmented generation (RAG), vector integrations, embedding workflows, and other AI/ML workloads. Experience in big data tooling such as Spark, Iceberg, EMR
• Fluent in Python and SQL; comfortable across structured and unstructured data
• Proven NLP experience: semantic search, entity recognition, concept normalization, embedding pipelines
• Strong grasp of inferential statistics and cohort methodology to be a real partner to data scientists and customers (as part of onboarding)
• Experience contributing to an AI/ML product, especially in automated research or scientific discovery
• Experience mentoring other engineers and providing technical leadership

Bonus Points

• Multi-cloud experience (AWS, Azure, GCP)
• Authorship or contribution to peer-reviewed publications or technical reports

Why Medeloop

• Ownership from day one: small team, high-trust, no layers between your work and its impact
• Technically ambitious: you'll build AI-powered workflows, not just support them
• Real-world stakes: your work accelerates drug development, addresses health equity, and improves clinical research for institutions that matter
• Strong foundation: Series A, top-tier investors, and a data asset (200M+ patient records) that most companies spend years trying to build

About the role

Responsibilities

• Build and advance our most sensitive and business-critical ML and GenAI models that power underwriting decisions and risk selection.
• Drive and execute ML projects end-to-end: problem framing, data exploration, feature engineering, model design, prototyping, offline/online evaluation, deployment, and monitoring.
• Design and implement ML pipelines for data preprocessing, feature engineering, model training, hyperparameter tuning, and model evaluation, enabling rapid and reproducible experimentation.
• Apply state-of-the-art ML and GenAI workflows (e.g., gradient-boosted trees, deep learning, LLMs, prompt engineering, transfer learning) to improve underwriting accuracy, automation, and decision support.
• Own model quality and robustness by defining success metrics, running ablations and diagnostics, and iterating to outperform prior baselines.
• Survey and incorporate recent advances in ML/GenAI research into our core underwriting capabilities, balancing scientific rigor with practical constraints.
• Collaborate closely with underwriters, product, data, and engineering partners to clarify requirements, align on tradeoffs, and ensure models integrate cleanly into production workflows.
• Communicate methods and results clearly through documentation, presentations, and design reviews; share learnings and patterns that level up the broader team.
• Contribute to a culture of scientific and data excellence by bringing mature empathy, best practices, and lightweight processes to experimentation, code review, and model governance.

Skills and Qualifications

• Ph.D. or MS in a quantitative or computational field (e.g., Computer Science, Statistics, Applied Math, Electrical Engineering) or equivalent practical experience.
• 5+ years of full-time experience developing and deploying ML- and data-based solutions in production.
• Practical, hands-on experience with supervised and unsupervised learning methods, including model selection, regularization, and calibration.
• Expertise in statistical analysis methods, especially regression analysis, statistical inference, and forecasting / time-series methods.
• Strong proficiency in Python and core ML libraries (e.g., scikit-learn, XGBoost/LightGBM, PyTorch/TensorFlow) and SQL for working with large, messy datasets.
• Experience with experiment design and evaluation (e.g., A/B tests, offline metrics vs. online KPIs, guardrails) and with ensuring reproducible results.
• Comfortable and effective in ambiguous problem spaces; demonstrated ability to own and drive projects with minimal oversight and process.
• Exceptional written and oral communication skills, with the ability to explain complex modeling choices and tradeoffs to technical and non-technical stakeholders.
• Minimum 1+ year of experience in insurance underwriting modeling (pricing, risk scoring, eligibility, or related applications).

Bonus Points

• Experience with modern GenAI techniques relevant to underwriting (e.g., using LLMs for document understanding, unstructured-to-structured extraction, or underwriter copilot workflows).
• Familiarity with model governance in regulated environments, including documentation, validation, monitoring, and change management.
• Experience with ML orchestration and MLOps tools (e.g., Airflow, Prefect, MLflow, SageMaker) for managing training, deployment, and monitoring at scale.
• Exposure to causal inference or uplift modeling for understanding the impact of underwriting or guideline changes.
• Experience working with cyber or P&C insurance data (exposures, limits, deductibles, claims, external risk signals).

Compensation

As a remote-first organization, our compensation reflects the cost of labor across several Canadian geographic markets. 

In Alberta, British Columbia & Ontario the base salary for this position ranges from $115,300.00/year up to $144,100.00/year. 

For all other locations, the base salary for this position ranges from $103,800.00/year up to $129,690.00/year. 

Consistent with applicable laws, an employee's pay within this range is based on a number of factors, which include but are not limited to relevant education, skills, job-related knowledge, qualifications, work experience, credentials, and/or geographic location. Your Recruiter can share more on the specific target salary range for your location during the interview process. Coalition, Inc. reserves the right to modify this range as needed.

Vacancy Status: This posting is for an existing vacancy.

Use of AI: We utilize AI-assisted tools to help us organize and review the high volume of applications we receive. However, our human recruiting team makes all final hiring decisions and interview selections, valuing personal connection over algorithms.

Application Updates: Consistent with our commitment to transparency, if you are interviewed for this role, we will provide a status update on your application within 45 days of your final interview.

Perks

• 100% medical, dental, and vision coverage
• Flexible PTO
• Annual home office stipend and WeWork access
• Mental & physical health wellness programs like Headspace, Lumino, and more!
• Competitive compensation and opportunity for advancement

Research Scientist, AI-powered scientific discovery

Location: Montreal, Canada

At Google DeepMind, we value diversity of experience, knowledge, backgrounds and perspectives and harness these qualities to create extraordinary impact. We are committed to equal employment opportunities regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy, or related condition (including breastfeeding) or any other basis as protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know.

Snapshot

We are looking for a Research Scientist to join our team in Montreal dedicated to AI for Scientific Discovery. Specifically, the team studies systems that combine code execution and retrieval tools with natural-language scientific knowledge to accelerate new scientific discoveries. Research includes, but is not limited to general purpose algorithms that leverage Large Language Model (LLMs) for efficient search and exploration, LLM fine-tuning with Reinforcement Learning, and open-ended tasks for large scale AI-powered empirical research.  

About us

Artificial Intelligence could be one of humanity’s most useful inventions. At Google DeepMind, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence. We use our technologies for widespread public benefit and scientific discovery, and collaborate with others on critical challenges, ensuring safety and ethics are the highest priority.

The role

Research Scientists at Google DeepMind lead our efforts in developing novel algorithmic architecture towards the end goal of solving and building Artificial General Intelligence.

In this role, responsibilities will include making key contributions into the latest research developed in Google DeepMind Scientific Discovery effort, such as:

Key responsibilities

• Design, implement and evaluate models, agents and software prototypes of large foundational models.
• Push the boundary of state of the art RL methods and machine learning optimization methods to build autonomous scientific discovery systems. 
• Report and present research findings and developments including status and results clearly and efficiently both internally and externally, verbally and in writing.
• Suggest and engage in team collaborations to meet ambitious research goals.
• Work with external collaborators and maintain relationships with relevant research labs and key individuals as appropriate.
• Work in collaboration with our Responsible AI teams to ensure our advances in intelligence are developed ethically and provide broad benefits to humanity.

About you

You are a passionate and talented researcher with a strong foundation and a proven ability to conduct impactful research in AI. You embrace change and thrive under ambiguity. You have a collaborative mindset and are excited to work as part of a team to tackle ambitious research challenges. You are passionate about seeing your research translated into real-world products that improve the scientific discovery process of users. You are eager to see your research contribute to real-world applications and are driven by a desire to create positive change through AI.

• PhD in Computer Science, Artificial Intelligence, or a related field.
• Strong publication record in top-tier machine learning conferences or journals.
• Solid understanding of deep learning, natural language processing, and/or reinforcement learning.
• Experience with Large Language Models, preferably in the context of code synthesis.
• Experience with relevant ML frameworks such as JAX, TensorFlow, or PyTorch.
• A real passion for AI!