Senior Product Manager, AI & Data
New
P
PLACEFintech, Mortgage Technology
Remote - United StatesFull-TimeSenior
Salary$90,000-150,000/year, depending on experience
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Job Details
- Experience
- 8+ years in product management, including 3+ years building and shipping AI/ML products in production
- Required Skills
- SQLProduct ManagementCross-functional Team LeadershipProduct AnalyticsData engineeringRESTful APIsLLM
Requirements
- 8+ years in product management, including 3+ years building and shipping AI/ML products in production.
- Proven track record delivering AI features end-to-end—from problem definition through deployment and iteration.
- Experience leading or mentoring AI and/or data engineers in a hands-on, player-coach capacity.
- Strong technical fluency: comfortable with APIs, model evaluation, and collaborating directly with ML engineers.
- Solid data engineering understanding: pipelines, schemas, data quality, and supporting infrastructure.
- Experience with LLM-based systems (prompting, RAG, document intelligence, etc.).
- Demonstrated ability to ship quickly, iterate in production, and learn through execution.
- Excellent product craftsmanship: writes clear specs, leads cross-functional alignment, and communicates with executives.
- Strong cross-functional leadership across Product, Engineering, and Customer Experience.
- Data-driven decision maker with clear prioritization discipline and ability to weigh tradeoffs.
Responsibilities
- Own and execute the AI product strategy and roadmap across document intelligence, workflow automation, and decisioning.
- Embed AI deeply into the mortgage Point of Sale, enhancing both borrower and loan officer experiences.
- Lead and develop a small team of AI and data engineers, setting direction and removing blockers.
- Partner cross-functionally with Product, Engineering, and Customer Experience to align AI initiatives with the broader product ecosystem.
- Design, scope, and ship AI-powered features from concept through production and iteration.
- Evaluate vendors, models, and tools with a strong perspective on build vs. buy tradeoffs.
- Establish frameworks for AI feature intake, prioritization, and delivery.
- Ensure strong data foundations by collaborating on pipelines, schemas, and data quality.
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