Manager of Data Science, Finance & Strategy

Robinhood·Menlo Park, CAsenior

In short

→Liderazgo técnico y de equipo en ciencia de datos para finanzas y estrategia.
→Desarrollo de análisis predictivos, encuestas y modelos de IA para decisiones empresariales.
→Se prioriza el trabajo presencial en Menlo Park 5 días/semana.

Fluency in English required for cross-team collaboration and reporting.

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What they ask for

✓Liderazgo comprobado de equipos de ciencia de datos y analistas.
✓Experiencia en NPS y encuestas de satisfacción con análisis de drivers.
✓Habilidades avanzadas en análisis de datos, modelos predictivos y métricas.
✓Capacidad para construir infraestructura escalable de pipelines y algoritmos.
✓Colaboración con equipos de finanzas, producto y operaciones.
✓Inglés fluido para comunicación con equipos multidisciplinarios.

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Who should you write to at Robinhood?

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Join us in building the future of finance. Our mission is to democratize finance for all. An estimated $124 trillion of assets will be inherited by younger generations in the next two decades. The largest transfer of wealth in human history. If you’re ready to be at the epicenter of this historic cultural and financial shift, keep reading. About the team + role We are building an elite team, applying frontier technologies to the world’s biggest financial problems. We’re looking for bold thinkers. Sharp problem-solvers. Builders who are wired to make an impact. Robinhood isn’t a place for complacency, it’s where ambitious people do the best work of their careers. We’re a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards. The Finance & Strategy Data Science team is a specialized Data Science group within Robinhood’s Finance & Strategy organization. Our mission is to use data, advanced analytics, machine learning, and AI to generate actionable insights, improve decision-making, and help Robinhood better understand and optimize business performance. We partner closely with teams across Robinhood including Finance, Product, Data, Engineering, Marketing, Research, and Operations. Our work spans customer and business analytics, benchmarking, forecasting, measurement frameworks, and AI-powered analytics, helping leaders make faster, more informed decisions and identify opportunities to improve performance across the company. As the Data Science Manager for Finance & Strategy, you will lead and develop a high-performing team of data scientists and analytics engineers, setting the team’s technical direction and priorities while partnering with leaders across Robinhood to turn complex business questions into actionable insights. You will oversee strategic analytics, measurement and benchmarking frameworks, deep-dive analyses, and scalable data science and AI solutions. You will play a key role in expanding how Finance & Strategy uses data and AI to enable faster, more predictive, and more effective decision-making.This role combines strong people leadership, technical depth, and business judgment to identify high-impact opportunities and elevate the quality and scalability of analytics across Robinhood. This role is based in our Menlo Park, CA office, with in-person attendance expected 5 days per week. At Robinhood, we believe in the power of in-person work to accelerate progress, spark innovation, and strengthen community. Our office experience is intentional, energizing, and designed to fully support high-performing teams. What you’ll do • Lead, mentor, and develop a high-performing team of data scientists and analytics engineers; operate as a player-coach who stays hands-on in the code and ships real work alongside the team • Foster a culture of technical excellence, strong business judgment, experimentation, and clear accountability across the team • Lead Robinhood's NPS program end to end: survey design, sampling and weighting, key driver analysis, verbatim analysis, and executive reporting • Oversee the broader survey and research portfolio, including safety perception, brand/product sentiment, benchmarking, and trend tracking • Make agentic workflows the default operating model. Build durable shared infrastructure so the team can delegate analysis, pipelines, QA, and recurring reporting to agents • Drive both sides of the analytics stack: rigorous data science (measurement, experimentation, modeling) and the analytics engineering foundation (pipelines, tooling, data models) • Partner close

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