Staff Data Scientist, ML (People Analytics & Insights)

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.

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 Talent Management & Analytics team is scaling to its next major milestone—integrating advanced predictive insights and tactical AI into our workforce systems. Our mission is to build the data solutions that help the entire company recruit exceptional talent, design high-performing team structures, and put active organizational insights directly into the hands of everyone making team decisions. Operating at the intersection of data science, product development, and organizational psychology, we are transforming how Robinhood uses data to empower our workforce and anticipate organizational needs.

As a Staff Data Scientist, you will serve as the team's technical and strategic anchor, owning the vision, design, and delivery of the high-impact data products that our executives, people partners, and line managers rely on every day. Your focus will be entirely on solving meaningful organizational problems: understanding what enables exceptional talent to thrive, accelerating team performance, and designing proactive strategies that support long-term retention across Robinhood. This is a unique opportunity to apply state-of-the-art language models and predictive analytics to solve critical talent problems!

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.

• →Spearhead the next phase of talent intelligence: Take the team beyond descriptive reporting and build the predictive frameworks that show not just what is happening in our workforce, but what will happen next.
• →Architect a unified workforce data model: Pipeline data across disparate recruiting systems, performance cycles, surveys, and internal tools into a single, cohesive data model that maps the entire employee lifecycle; use AI-native workflows aggressively to parse unstructured text like exit notes and survey feedback at scale.
• →Define the metrics dictionary and semantic layer: Standardize how metrics like headcount, attrition, and workforce trends are measured across the company; build the semantic layers that keep this data consistent and trustworthy at scale.
• →Drive data access control and governance: Build the frameworks for role-based access controls and data masking so HR, Finance, and line managers have exactly the access they need without risking sensitive personnel data.
• →Redesign employee sentiment architecture: Replace traditional annual survey cycles with a continuous, always-on listening framework that captures real-time organizational health and highlights leading risk indicators.
• →Own product delivery: Act as the technical product owner for internal data interfaces—collaborating directly with Enterprise Engineering to ensure exceptional user utility, predictive accuracy, and data reliability.
• →Enforce rigorous statistical standards: Apply experimental design, A/B testing, and psychometric methodologies to ensure our predictive workforce models and survey frameworks are scientifically backed and statistically valid.
  
  

• 7+ years of data science experience, with advanced skills in Python, SQL, and direct experience with modern data infrastructure like Snowflake, BigQuery, or dbt.
• Strong product focus and experience defining user requirements, collaborating with engineers, and taking internal data tools from conception to launch.
• Deep understanding of statistics, experimental design, and psychometrics or survey methodology to design research-backed questions.
• Direct experience integrating language models into data pipelines to automate text processing and summarization at scale.