Data Scientist, People

Requirements and responsibilities

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In this role, you will

Build the analytical foundation to evaluate compensation competitiveness. Connect 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. Example: a regretted attrition model that flags at-risk employees 90 days in advance and surfaces the underlying signals directly into manager 1:1 prep.
Design and deploy AI agents that draft first-pass recommendations for high-stakes People decisions, including compensation, promotion, and hiring. People leaders review and adjust rather than starting from scratch.
Build the recruiting analytics layer that connects sourcing channel to time-to-hire to first-year performance to tenure. Use it to reallocate recruiting spend and surface weekly insights to recruiting leadership.
Analyze organizational effectiveness, including spans and layers, talent density, and hiring efficiency. Identify where the org is over-leveled, under-leveled, or structurally inefficient.
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, not on a quarterly schedule.
Support high-stakes organizational and talent decisions with rigorous analysis, including executive hiring, retention, and reorganizations.

Required Skills & Experience

Minimum 6 years of experience. Targeting Senior or Staff Data Scientist depending on demonstrated scope and impact.
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

Preferred Qualifications

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

Bonus Points

Experience building on Replit
Experience with NLP or unstructured text analysis
Interest in the future of AI-native organizations and how AI changes the way companies operate