People Analytics Lead - Recruiting

What You'll Achieve

• Own recruiting analytics end-to-end: Build and maintain the dashboards and reporting infrastructure Recruiting leadership relies on (funnel health, conversion rates by stage, time-to-fill, offer acceptance, pipeline aging, interview/interviewer effectiveness, and recruiter capacity) — real-time, self-serve, and trusted.
• Build headcount + velocity models: Translate hiring targets into pipeline math (volume needed by stage, ETA to close, roles trending behind plan) and tie it to recruiter capacity and prioritization — so leaders see issues early and know what to do about them.
• Make talent data decision-grade: Define core recruiting metrics and their definitions, document them, and build toward a single source of truth the team can align on.
• Bring signal to pipeline leadership reviews: Show up to pipeline reviews and QBRs with crisp analysis and a recommendation (where the bottleneck is, what changed, what to test next), not just a slide deck.
• Improve the recruiting system: Use data to spot where process, calibration, or prioritization is creating drag — and partner with Recruiting and Finance to fix it.
• Use AI as leverage in your first 90 days: Use AI to accelerate query iteration, metric QA, and insight narratives (e.g., draft + test SQL approaches, anomaly checks, and concise readouts) while keeping the underlying logic and definitions clean and auditable.

Skills You'll Need to Bring

• Proven ability to build analytics that gets used: You’ve shipped dashboards/models that teams rely on to run real weekly decisions — not one-off reports.
• Strong SQL + data modeling judgment: You can write complex queries, debug data issues, and build maintainable models/pipelines that other people can trust.
• Quantitative modeling for recruiting problems: You can translate hiring stages into clear funnel math and capacity views, and you know how to stress-test assumptions.
• Strategic partner instincts: You anticipate the questions leaders will ask, show up with the analysis, and can say what the data implies for decisions.
• AI-fluent, with good taste: You use AI as a daily multiplier (analysis, QA, drafting narratives) and know when to rely on it vs. when to go to first principles.
• Build in Notion: You’re comfortable structuring knowledge and workflows in Notion (databases, views, documentation) so your work is easy for others to find, trust, and build on.