Staff Data Scientist, ML (people Analytics & Insights)

Robinhood Robinhood · Fintech · Chicago, IL +2 · ENG Data Science

Staff Data Scientist role focused on building predictive frameworks and integrating AI into workforce systems for talent management and insights. This involves architecting data models, defining metrics, and redesigning employee sentiment analysis using language models and predictive analytics.

What you'd actually do

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.

Skills

Required

  • Python
  • SQL
  • Snowflake
  • BigQuery
  • dbt
  • product focus
  • user requirements
  • collaboration with engineers
  • internal data tools
  • statistics
  • experimental design
  • psychometrics
  • survey methodology
  • language models
  • text processing
  • summarization

What the JD emphasized

  • AI-native workflows aggressively to parse unstructured text
  • integrating language models into data pipelines to automate text processing and summarization at scale

Other signals

  • applying frontier technologies
  • integrating advanced predictive insights and tactical AI
  • state-of-the-art language models and predictive analytics