Replit is the agentic software creation platform that enables anyone to build applications using natural language. With millions of users worldwide, Replit is democratizing software development by removing traditional barriers to application creation.
At https://replit.com?utm_source=chatgpt.comReplit, we’re building an AI-native company — and that includes how we operate internally.
We’re looking for a Data Scientist, People to help us build the intelligence systems behind hiring, compensation, performance, organizational design, and workforce planning.
This is not a traditional People Analytics role focused on dashboards and reporting. We want someone who can use data, AI, and automation to help the company make faster and better talent decisions at scale.
You’ll work across HR, Recruiting, Finance, and Data Engineering to build models, tools, and workflows that improve how the company hires, rewards, retains, and organizes talent.
You’ll report into Data Science and partner closely with the People leadership team.
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.