About the Role:

You'll be Recruiting's dedicated people analytics partner - owning the data, shaping the strategy, and in the room where decisions get made. The core of the role is building trusted recruiting analytics infrastructure (funnel + stage conversion, pipeline aging, time-to-fill, offer acceptance, sourcing channel quality, and recruiter capacity) and making it self-serve for recruiters and leaders. You'll own the stack end-to-end - from raw data in Ashby and Snowflake to clear insights that hold up in a pipeline review.

You're the person who turns workforce signals into recruiting strategy - bringing a point of view.

This role may be based in either our San Francisco or New York City offices. We work from our offices on Mondays, Tuesdays and Thursdays (our Anchor Days) because we do our best thinking and building together in person. We're looking for someone who's excited to work alongside the team during those days.

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.

Notion is committed to providing highly competitive cash compensation, equity, and benefits. The compensation offered for this role will be based on multiple factors such as location, the role's scope and complexity, and the candidate's experience and expertise, and may vary from the range provided below. For roles based in San Francisco or New York City, the estimated base salary range for this role is $210,000 - $260,000 per year.

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A Note on AI

You don't need deep AI expertise for every role, but we do expect every Notino to be intellectually curious, drawn to tinkering and discovery, and excited to use AI as a real collaborator in their work. For some roles, AI fluency is a core requirement - when that's the case, we'll say so explicitly in the qualifications. People who thrive here don't treat AI as a novelty. They use it to think better, and make their work easier for others to build on.