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Responsibilities
- Define the Data Science operating model across a portfolio of platforms — deciding what the team should look like at each stage of scale and where to invest across diverse problem spaces including growth/personalization, social good, design craft, communications, and globalization.
- Partner with leadership teams across product, engineering, and go-to-market organizations to build and strengthen a data-inspired culture, bringing innovative data insights into the key decisions for product success.
- Inspire, lead, and grow a globally distributed team of data scientists, setting the hiring bar, standards, and culture for analytical rigor.
- Own measurement and experimentation strategy at scale — holdouts, incrementality, and topline attribution across platforms driving tens of millions of incremental users.
- Lead the measurement and evaluation flywheel for AI-powered marketing initiatives, including quality rubrics, golden-set evals, governance, and revenue tie-out.
- Apply your expertise directly and through coaching the team to shape growth strategy, product optimization, design quality evaluation, communications optimization, and AI-driven automation tradeoffs.
- Build strong cross-functional relationships with product and engineering leadership that make Data Science a co-pilot, not an afterthought — presenting regularly to senior leadership with crisp, actionable, defensible insights.
- Drive the evaluation and quality mindset for AI systems — measuring whether AI/ML systems are delivering on reasoning quality, task completion, and user trust.
- Build organizational processes and structure for scale including recruiting, new hire onboarding, team operating rhythm, and people programs.
- Lead analytics through change as platforms are rebuilt to be AI-native, setting the example for AI-native analytics practices.
Minimum Qualifications
- 10+ years of work experience managing analytics teams, working collaboratively with Product and Engineering teams, and guiding data-influenced product and business planning, prioritization, and strategy development
- Deep expertise in causal measurement, experimentation design, holdouts, incrementality, and topline attribution at scale
- Demonstrated experience in hiring, retaining, and scaling diverse, high-performing teams
- Proven experience influencing strategy and driving change across org boundaries through clear and compelling communication of data-driven insights and analyses
- Strong executive communication skills with experience presenting to VP/exec-level leadership
- Experience evaluating AI/ML system quality — reasoning quality, task completion, and user trust metrics
Preferred Qualifications
- Experience leading analytics across multiple, very different problem spaces (e.g., growth, social good, design, communications, globalization)
- Experience driving analytics for rapidly scaling or AI-native product lines
- Track record as a hands-on technical leader who sets the bar for analytical rigor and can still do the hardest analysis themselves
- Experience with both product analytics and business analytics applications
- Heavy user of AI tools with a mindset toward AI-native analytics workflows
- Previous experience building a data science function from a small, senior team to a scaled organization
$253,000/year to $314,000/year + bonus + equity + benefits
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