Manager of Data Science, Finance & Strategy
About this role
Leading and developing a high-performing group of data scientists and analytics engineers forms the core of daily responsibilities, while acting as a player-coach who remains hands-on in code to ship real work. The Finance & Strategy Data Science team applies frontier technologies, advanced analytics, and artificial intelligence to address major financial problems and optimize business performance across Robinhood. Team members build measurement frameworks, drive the NPS program, oversee survey portfolios, and implement agentic workflows to handle recurring reporting and data pipelines.
Operating within the Finance & Strategy organization, the team partners closely with product, engineering, finance, and marketing departments to transform complex business questions into actionable insights. Success in the position requires a blend of people leadership, technical depth, and strong business judgment to elevate analytics scalability. Candidates who thrive here are bold thinkers and sharp problem-solvers capable of driving both rigorous data science and the analytics engineering foundation.
What you will do
- Lead, mentor, and develop a team of data scientists and analytics engineers while operating as a player-coach who writes code and ships real work.
- Manage Robinhood's Net Promoter Score program from end to end, covering survey design, sampling, weighting, key driver analysis, and executive reporting.
- Oversee research and survey portfolios including safety perception, product sentiment, trend tracking, and benchmarking.
- Build shared infrastructure and agentic workflows to delegate analysis, pipelines, QA, and recurring reporting to autonomous agents.
- Drive both rigorous data science methodologies and the foundational analytics engineering pipelines, tooling, and data models.
- Partner with finance, product, engineering, and operations teams to turn complex business questions into scalable analytics solutions.
What you need
- Minimum of 7 years of professional experience in analytics, data science, or related fields.
- At least 2 years of direct people management experience leading technical teams.
- Technical expertise in Python, SQL, statistical analysis, experimentation, and advanced analytics.
- Demonstrated ability to translate complex business questions into rigorous analyses and actionable data solutions.
- Experience building measurement frameworks, predictive models, benchmarking systems, and strategic analytical approaches.
- Strong business judgment and communication skills for influencing cross-functional stakeholders and senior leadership.
Benefits
- Market competitive and pay equity-focused compensation structure
- 100% paid health insurance for employees with 90% coverage for dependents
- Annual lifestyle wallet for personal wellness and learning and development
- Lifetime maximum benefit for family forming and fertility benefits
- Dedicated mental health support for employees and eligible dependents
- Generous time away including paid time off, holidays, sick time, and parental leave
Skills and tech stack
Questions candidates ask
What is the remote work policy for this role?
This role is based in the Menlo Park, CA office, with in-person attendance expected 5 days per week. Robinhood believes in the power of in-person work to accelerate progress, spark innovation, and strengthen community.
What is the salary range for this position?
The expected base pay range for Zone 1 locations, which includes Menlo Park, CA, is $202,000 to $238,000 USD. Base pay depends on factors such as education, training, experience, location, business needs, and market demands.
What technologies and tools will I use?
The technical stack includes SQL, Python, statistical analysis, experimentation, and advanced analytics. Team members also build agentic workflows and shared infrastructure for automated pipelines and recurring reporting.
What teams will I collaborate with?
The role partners closely with cross-functional teams across Robinhood including Finance, Product, Data, Engineering, Marketing, Research, and Operations. You will also represent Finance & Strategy Data Science in leadership forums.