Data Scientist, Product

What You'll Do

• Embed with Product and Engineering teams as a trusted analytical partner, identifying opportunities to improve user experience, adoption, retention, and business impact.
• Define and maintain core metrics that create a shared understanding of product performance and customer value.
• Design and evaluate experiments, including A/B tests and causal inference methods, to measure the impact of product, model, and workflow changes.
• Analyze product usage, customer segments, and go-to-market signals to surface insights, size opportunities, and inform roadmap decisions.
• Build dashboards, reports, and self-serve tools that help teams answer product questions with confidence.
• Develop models, forecasts, and analytical frameworks to explain user behavior, detect anomalies, and guide prioritization.
• Translate complex analyses into clear recommendations for technical, business, and executive audiences.
• Partner with Engineering and Data teams to improve the infrastructure that powers analytics, experimentation, and decision-making.
• Establish the standards, practices, and culture for Product Data Science at Harvey.

What You Have

• 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.

Bonus

• 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.