5+ years of experience in data science, product analytics, economics, statistics, or another quantitative field, ideally in a high-growth product company, AI company, research organization, or similarly ambiguous environment
A strong track record of using SQL, Python, and statistical methods to answer product questions and turn analysis into product or business impact
Experience defining new metrics and measurement frameworks from scratch, especially for products where usage patterns, customer value, or success criteria are still being discovered
Deep fluency in experimentation, causal inference, A/B testing, and statistical modeling, with good judgment about when precision matters and when directional clarity is enough
Strong product instincts and curiosity about how users adopt, evaluate, and expand their use of AI-enabled workflows
Excellent written and verbal communication skills, including the ability to influence Product, Engineering, Go-to-Market, and executive stakeholders through clear reasoning and compelling data stories
Comfort creating structure in fast-moving, ambiguous environments and raising the quality of decision-making for the teams
Experience with AI/ML products, large language models, developer tools, enterprise software, or products used in complex professional workflows
Experience as an early data science or analytics hire at a hyper-growth startup, including helping define team norms, tooling, and best practices
Experience supporting enterprise or B2B products, including analysis of adoption, engagement, retention, expansion, or go-to-market motion
Familiarity with modern data infrastructure and the practical tradeoffs involved in building reliable analytics in a rapidly evolving product environment