Data Scientist, People

Replit Replit · Enterprise · Foster City, CA · Engineering

This role focuses on building AI and LLM-powered systems for internal HR and talent decisions, including compensation, hiring, and workforce planning. It involves developing predictive models, AI agents for decision support, and analytical frameworks to improve talent management at scale within an AI-native company.

What you'd actually do

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. Use LLMs and agentic workflows to analyze unstructured People data at scale, including support tickets, exit interviews, performance reviews, and engagement survey responses.

Skills

Required

  • 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 have

  • 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
  • Interest in the future of AI-native organizations and how AI changes the way companies operate

What the JD emphasized

  • AI and LLMs in analytics workflows
  • AI agents
  • agentic workflows
  • LLMs

Other signals

  • AI-native company
  • build models, tools, and workflows
  • AI agents that draft first-pass recommendations
  • LLMs and agentic workflows
  • always-on agents