About the role
Why Harvey At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. With 1500+ customers in 60+ countries, strong product-market fit, and world-class investor support, we’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. Role Overview 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.
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.
