Senior Data Scientist, Product

About the role

Robinhood is seeking a Senior Data Scientist to join their Product team in Menlo Park, California. This role centers on using data science approaches to guide product decisions and improve the experience for users on the platform. You will work alongside product managers and engineers to study how people interact with the product and identify opportunities for meaningful improvement. The position requires taking ownership of analytical projects and helping shape product direction through evidence-based insights and rigorous analysis. As a senior member of the team, you will play a key role in setting the analytical standards and methodologies that drive product growth. You will also collaborate across functions to ensure that data insights are integrated into every stage of the product development lifecycle.

Key facts

What you'll do

• Partner with product teams to define and measure key performance indicators for product features.
• Design and run experiments to evaluate the impact of product changes on user behavior.
• Build statistical models to understand user engagement patterns and identify retention drivers.
• Analyze large-scale user interaction data to uncover trends and generate actionable insights.
• Present findings through clear visualizations and written reports to product and business stakeholders.
• Develop forecasting models to anticipate product performance metrics and user growth trajectories.
• Work with engineering teams to build data pipelines and measurement frameworks for products.
• Perform causal inference analyses to understand the true effect of product interventions.
• Create dashboards and monitoring tools to track product health metrics over time.
• Guide junior data scientists and help establish best practices within the team.
• Identify opportunities for product optimization through segmentation analysis and cohort studies of user behavior.
• Support the product team in prioritizing features by quantifying the expected impact of each initiative.

Requirements

• Strong foundation in statistics, machine learning, and experimental design principles and methods.
• Experience working with large datasets and performing thorough exploratory data analysis.
• Proficiency in at least one programming language commonly used for data analysis work.
• Demonstrated ability to translate business questions into structured and rigorous analytical frameworks.
• Experience communicating complex analytical results to non-technical business stakeholders and partners.
• Bachelor's degree in a quantitative field or equivalent practical experience in data science.
• Track record of delivering data-driven product recommendations that meaningfully influenced product decisions.
• Ability to work independently and manage multiple projects with competing priorities at once.
• Experience with large-scale data infrastructure and distributed computing environments for processing analytical workloads.
• Strong written and verbal communication skills for presenting findings and influencing product strategy.

Nice to have

• Familiarity with product analytics platforms and experimentation frameworks commonly used in consumer technology companies.
• Experience with Bayesian statistical methods or causal inference techniques for product measurement and evaluation.
• Background in financial technology or consumer-facing digital products with large and active user bases.
• Contributions to open source data science tools or published research in relevant areas of study.
• Knowledge of regulatory environments and compliance considerations relevant to consumer financial products.

Skills & tools

• Statistical modeling and hypothesis testing frameworks for product analysis and experimentation
• Programming languages for data analysis, manipulation, and automation of analytical workflows
• Data visualization and reporting platforms for effective stakeholder communication and insight sharing
• Experimentation and A/B testing methodologies for making informed product decisions
• SQL for querying large relational databases and data warehouses at scale
• Version control and collaborative coding environments for team-based data science work
• Python or R for statistical computing and reproducible research workflows in data science

Practical notes

• This role is based in Menlo Park, California and requires on-site presence at the office location.
• The hiring process includes multiple rounds of interviews focusing on both technical skills and product thinking abilities.
• Candidates should be prepared to discuss past projects and analytical approaches in detail during each interview stage.
• Robinhood is an equal opportunity employer and considers all qualified applicants without regard to background or identity.
• Remote work is not available for this position as it is based in the Menlo Park office.
• The team values diverse perspectives and encourages candidates from all backgrounds to apply.