Staff Data Scientist, Talent Intelligence & AI

Unchain Data; Chicago, Menlo Park, New York (IL, CA, NY); Hybrid; USD 191,000 - 224,000; Full time

Benefits offered by this job

Health insurance

Equity ownership

401(k) matching

Paid time off

Catered meals

Job summary

Robinhood seeks a Staff Data Scientist to drive the vision and delivery of high-impact data products. You will own predictive talent insights, unify workforce data, and apply AI/ML techniques across HR platforms.

This role blends data science, product sense, and organizational psychology to enable better decisions and retention. You’ll work across offices in Menlo Park, Chicago, and New York with in-person collaboration at least three days a week, shaping scalable data solutions for the

Qualifications

• 7+ years of data science experience with Python and SQL.
• Experience with modern data infrastructure (Snowflake, BigQuery or dbt).
• Strong product focus and cross-functional collaboration.
• Experience integrating language models into data pipelines.

Responsibilities

• Spearhead talent intelligence and predictive frameworks for workforce data.
• Architect a unified data model across recruiting, performance, surveys, and tools.
• Define metrics and semantic layers for consistent HR analytics.
• Drive data access governance and data masking for sensitive info.
• Redesign employee sentiment architecture with continuous listening.
• Own data interface delivery with Enterprise Engineering collaboration.

Skills

Data science

Python

SQL

ML/AI integration

Tools

Snowflake

BigQuery

dbt

Job description

Robinhood seeks a Staff Data Scientist to drive the vision and delivery of high-impact data products. You will own predictive talent insights, unify workforce data, and apply AI/ML techniques across HR platforms.

This role blends data science, product sense, and organizational psychology to enable better decisions and retention. You’ll work across offices in Menlo Park, Chicago, and New York with in-person collaboration at least three days a week, shaping scalable data solutions for the