Head of Data

We don't need a Head of Dashboards. We need a Head of Decisions.

As Head of Data Science, you own how we use ML, experimentation, and causal inference to drive product + business strategy at billion-user scale. Your team's models decide what 1B+ people see, who we approve for credit, what content we block, and where we invest $100Ms.

This is 01 org build. The mandate is real. The headcount is approved. We need you to define what world-class Data Science means here.

What You'll Own – Expanded Scope

1. The Science Portfolio

• Product DS: Experimentation, feature impact, user understanding. Every product team has a DS partner who co-owns metrics
• Growth DS: Acquisition, activation, retention, resurrection. LTV models, channel optimization, incentive design
• Monetization DS: Ads ranking, auction theory, pricing, revenue forecasting, advertiser/merchant science
• Trust & Safety DS: Fraud, abuse, risk scoring, content moderation, account integrity. ML that keeps 1B users safe
• Recommendations: Feed, search, discovery. Multi-stage rankers, exploration/exploitation, long-term user value
• Core Modeling: Churn prediction, forecasting, causal inference, uplift modeling, marketplace optimization

2. Science Excellence – How We Work

• Rigor: Set bar for experimentation, causal inference, and offline evaluation. No p-hacking. No cherry-picking metrics
• Velocity: P50 time from idea prod experiment < 3 weeks. Kill projects fast when they don't work
• Innovation: 20% time on 01 bets. Publish internally. Open source when it makes sense. Stay ahead of SOTA
• Measurement: Your team defines the North Star metrics. You prevent Goodhart's Law. You call BS on vanity metrics

3. ML in Production – End-to-End Accountability

• You don't throw models over the wall. Your team owns offline eval online experiment monitoring iteration
• Partner with Head of AI Eng on infra needs. Partner with Head of Data on feature/data quality
• Sign off on go/no-go for models impacting >10M users or >$10M revenue
• Post-launch reviews: Did we move metrics? What did we learn? What's v2?

4. Team & Culture

• Talent bar: Your team is why other DS want to join. Staff scientists here could be Heads of DS elsewhere
• Career paths: Build IC track to Principal/Distinguished. Make this the best place to grow as a technical scientist
• Collaboration: No silos. Your team embeds with Product/Eng but maintains scientific independence
• Science culture: Paper reading groups, internal conferences, tech talks. Intellectual honesty > politics

5. Exec Partnership & Strategy

• C-level advisor: Sit in product + business reviews. Answer: What should we build? with data, not opinions
• Roadmap influence: 30% of company roadmap comes from insights your team generated
• Resource allocation: Defend headcount with ROI. Kill low-impact work. Focus org on 10x bets
• External face: Represent company at NeurIPS/KDD/recruiting events. Make us a DS destination

What You'll Bring – Senior Bar

Must-haves:

• 12+ YOE in Data Science/Applied ML, with 5+ YOE leading DS orgs of 25+ people at scale
• You've shipped: Models you led are running in prod at 100M+ user scale. You know what breaks
• Technical depth: PhD or equivalent in ML/Stats/Econ/CS. Still credible in a tech review with Principal scientists
• Breadth: Led 3+ of: Growth, Recsys, Ads, Trust & Safety, Forecasting. You can context switch and go deep
• Experimentation zealot: Bayesian methods, CUPED, sequential testing, interference. You've seen A/B tests go wrong 100 ways
• Product + business acumen: You've said no to execs because the data didn't support it. And you were right
• Hiring magnet: Staff+ scientists left FAANG to work for you. Retention >90% for top performers