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Replit

Senior

Build AI-native intelligence systems behind Replit's hiring, comp, and workforce decisions

Replit is hiring a Data Scientist, People to move beyond dashboards and build predictive models and AI agents that directly recommend compensation adjustments, flag attrition risk, and inform hiring and org-design decisions. The role requires strong SQL/Python, causal inference and experimentation depth, and hands-on use of LLMs on unstructured People data (exit interviews, performance reviews). Expect the interview to test statistical rigor, applied AI-agent design for high-stakes decisions, and comfort handling sensitive compensation/org data.

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这场面试考什么

  • Compensation and workforce predictive modeling (attrition risk, offer competitiveness)
  • Statistical rigor: experimentation design and causal inference
  • Designing AI agents for high-stakes People decisions with human review
  • Applying LLMs to unstructured People data (exit interviews, performance reviews)
  • Organizational effectiveness analysis (spans, layers, talent density)
  • Handling sensitive compensation/org data with discretion; executive communication

常见提问方向

How would you build a regretted-attrition model that flags at-risk employees 90 days in advance?

Walk me through connecting offer/band/acceptance data into a live compensation-adjustment system

How would you design an AI agent that drafts first-pass promotion or comp recommendations for leaders to review?

Describe how you'd apply causal inference to a People Analytics question rather than just correlational reporting

How would you use LLMs to extract signal from exit interviews or performance review text at scale?

How do you handle highly sensitive compensation or org data and communicate findings to executives?

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