Product Strategist

Raydar•New York, NY

2d•$185,000 - $250,000•Hybrid

About The Position

Our client is a cash flow positive AI venture studio that combines strategy, product development, and long term value creation. The team works inside PE backed and mid market companies to redesign workflows around AI, then builds and helps adopt the products those businesses need. The studio has about 40 to 45 people and is growing as new ventures and transformation programs begin. The role We are hiring a Product Strategist to lead the discovery and workflow analysis behind new venture builds. You will interview stakeholders, map how work and value move through a business, sequence the highest impact use cases, and turn those findings into product specifications that engineers can build against. You will stay involved through evaluation, adoption, and the decisions that shape each product.

Requirements

One of two relevant profiles: 3 to 7 years across top strategy consulting, private equity, or venture capital plus hands on startup, product, or operator experience; or 7 to 12 years at a top strategy, private equity, or venture capital firm with principal or partner level responsibility.
Demonstrated product judgment and the ability to translate business needs into buildable requirements.
Hands on use of AI tools, coding, GitHub projects, or side builds, rather than theoretical familiarity alone.
Strong business acumen and experience working with senior executives and technical teams.
A bachelor's degree from a top tier university.
A coherent career narrative with clear ownership, measurable impact, and no pattern of unexplained short stays.

Nice To Haves

Technical fluency in Python, data analysis, AI feasibility, or system design.
Experience building or deploying products inside a startup or technology company.
Experience with venture building, business transformation, or value creation for PE backed companies.

Responsibilities

Lead stakeholder and operator interviews and turn unstructured input into clear findings.
Map workflows at the task level and identify where the largest value pools sit.
Sequence AI use cases by value, feasibility, and data readiness, then defend the ordering.
Write priority questions, acceptance criteria, data contracts, and clean product specifications.
Help define golden datasets, evaluation datasets, success measures, and time to answer benchmarks.
Run demos, pre reads, follow ups, training, and feedback loops that drive real adoption.
Maintain concise decision records so product choices stay traceable after each meeting.