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Shepherd is a technology-driven Managing General Underwriter (MGU) transforming commercial Property & Casualty insurance for high-hazard industries. The company recently raised a $42M Series B (March 2026, total funding $60M+) led by Intact Private Capital and backed by Costanoa Ventures, Spark Capital, Susa Ventures, and Y Combinator.
You will own the product roadmap, execution, discovery, and vision for Nova, Shepherd's autonomous underwriting platform. Nova uses AI agents to automate the end-to-end underwriting process: reading broker submissions (applications, claims history, payroll records, prior policies), extracting hundreds of facts, assessing risk, and pricing accounts. The team structures progress on an autonomy ladder (L1–L5), currently in the middle stages, working to expand the set of accounts the system handles unsupervised.
This is Nova's first product hire. You'll work alongside a small, high-caliber engineering team that has already taken autonomous underwriting from concept to real prices on real accounts, plus underwriters with decades of pricing expertise and leadership setting business direction. Your role sits at the intersection of three domains: engineers focused on eval runs and retrieval quality, underwriting judgment, and business strategy.
Key responsibilities include owning the product end-to-end (vision, discovery, prioritization, execution, UX/design decisions), understanding underwriter workflows deeply, ruthlessly prioritizing what to build and what not to build, and being accountable for outcomes: adoption by underwriters, autonomy percentage, accuracy, and deal-closing capability on fully autonomous motions. You'll pair with engineers on extraction flows, put working versions in front of underwriters same-day, and define what autonomous underwriting looks like for the industry.
This role is for someone closer to a founder than a traditional PM—someone who thrives on intellectual challenge, progress, and impact. The team expects you to move fast and get up to speed quickly on a problem they know deeply.
