Data Scientist, People

Responsibilities

• Build the analytical foundation to evaluate compensation competitiveness by connecting Ashby offer data, band position, acceptance rates, and market benchmarks into a live system that recommends specific adjustments
• Develop predictive models and tooling that help managers and recruiters make better decisions faster (e.g., regretted attrition model that flags at-risk employees 90 days in advance)
• Design and deploy AI agents that draft first-pass recommendations for high-stakes People decisions, including compensation, promotion, and hiring
• Build the recruiting analytics layer that connects sourcing channel to time-to-hire to first-year performance to tenure, and use it to reallocate recruiting spend
• Analyze organizational effectiveness, including spans and layers, talent density, and hiring efficiency
• Partner with Finance to move from spreadsheets to live workforce model that accounts for attrition, hiring velocity, and ramp time by function
• Use LLMs and agentic workflows to analyze unstructured People data at scale, including support tickets, exit interviews, performance reviews, and engagement survey responses
• Replace recurring reporting cycles with always-on agents that surface insights to leaders when they need them
• Support high-stakes organizational and talent decisions with rigorous analysis, including executive hiring, retention, and reorganizations

Requirements

• Minimum 6 years of experience
• Experience in People Analytics, compensation analytics, or workforce analytics
• Strong SQL and Python skills
• Experience building predictive models and analytical frameworks for business decision-making
• Strong statistical foundation, including experimentation and causal inference
• Experience working with large-scale operational or behavioral datasets
• Demonstrated experience using AI and LLMs in analytics workflows
• Ability to communicate complex insights clearly to executives and cross-functional partners
• High ownership mindset and comfort operating in fast-moving environments
• Ability to handle highly sensitive organizational and compensation data with discretion

Nice-to-Haves

• Experience at a high-growth or AI-native company
• Experience building internal tools, agents, or automated workflows
• Familiarity with organizational design, compensation, or talent management concepts
• Experience with modern data stack tools (dbt, BigQuery, Snowflake, etc.)
• Experience with People systems such as Rippling, Ashby, Lattice, or Carta
• Experience building on Replit
• Experience with NLP or unstructured text analysis