Data Scientist, Product

Harvey•San Francisco, CA

2d•$155,000 - $240,000

About The Position

As a Product Data Scientist, you will be a core member of Harvey's product development organization and one of the foundational members of the Data Science function. You will partner with Product, Engineering, Design, Go-to-Market, and company leadership to define how we measure product success, understand user behavior, and make high-quality decisions in a fast-moving environment. This role is both strategic and hands-on. You will define north-star and product-level metrics, design experiments and causal analyses, build source-of-truth reporting, and turn ambiguous product questions into clear recommendations. You should be comfortable operating where the data is imperfect, the product is evolving quickly, and the right answer requires both analytical rigor and strong product judgment.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.