Head of Data

We dont 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 teams 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 Youll 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 dont 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 Goodharts Law. You call BS on vanity metrics

3. ML in Production End-to-End Accountability

• You dont 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 Whats 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 Youll Bring Senior Bar

Must-haves:

• 12 YOE in Data Science/Applied ML with 5 YOE leading DS orgs of 25 people at scale
• Youve 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. Youve seen A/B tests go wrong 100 ways
• Product business acumen: Youve said no to execs because the data didnt support it. And you were right
• Hiring magnet: Staff scientists left FAANG to work for you. Retention >90% for top performers