Harvey
Data Scientist, Product
- Jenis pekerjaan
- Penuh waktu
- Cara kerja
- Hibrida
- Lokasi
- San Francisco
- Dipublikasikan
- 19 Jun 2026
- Terakhir diperiksa
- 26 Okt 2026
Tentang posisi ini
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. We have 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.