Senior Product Manager, AI & Data

Role Description

This is a player-coach manager role. You will own execution directly and build the AI product and engineering function over time, starting with a small team reporting to you. You define direction by shipping, not by delegating.

• You have taken AI products from idea to production and understand how to work across data, models, and product constraints to ship reliably.
• You are strong on the data engineering side and can reason about pipelines, schemas, and data quality as naturally as you reason about model outputs.
• You can write a sharp product spec, pressure-test an ML pipeline with an engineer, and explain the tradeoffs to an executive in the same afternoon.
• You ship fast and make pragmatic decisions about when to build versus leverage third-party solutions, balancing speed, cost, and long-term capability.
• You are comfortable operating in ambiguity and working with imperfect data and evolving systems without slowing down execution.
• You have managed or mentored AI engineers and data engineers and know how to get the best out of a small technical team.

What You Will Own

• AI product strategy and roadmap across document intelligence, workflow automation, and data-driven decisioning, with our mortgage Point of Sale as the flagship integration surface.
• Direct management of AI engineers and data engineers, setting direction, removing blockers, and developing the team.
• Deep AI integration into the POS, ensuring AI capabilities are embedded in the borrower and loan officer experience rather than sitting alongside it.
• Coordination across AI and data engineering to design, scope, and ship production systems at speed.
• Peer relationships with Product, Engineering, and Customer Experience, keeping AI aligned with the rest of the product surface.
• Vendor and model evaluation against cost, performance, and build vs. buy considerations.
• Feature intake and prioritization, including how AI feature requests are evaluated, sequenced, and delivered.

Qualifications

• 8+ years in product management, with at least 3 years focused on AI/ML products in production.
• Demonstrated experience shipping AI-powered features end-to-end, from problem definition through model deployment and evaluation.
• Experience managing or mentoring AI engineers or data engineers directly.
• Strong technical fluency: Can read API docs, evaluate model outputs, and collaborate with ML engineers without a translator.
• Strong data engineering fluency: Can reason about pipelines, schemas, data quality, and the infrastructure AI products depend on.
• Experience with LLM-based systems, including prompt design, RAG architectures, document intelligence, or similar.
• Track record of shipping AI features quickly, with a bias for learning through production deployment.
• Writes crisp product specs, leads cross-functional reviews, and presents to executives.
• Proven ability to manage peer relationships across Product, Engineering, and Customer Experience.
• Data-driven prioritization discipline: Knows how to evaluate impact, weigh tradeoffs, and say no.

Nice to haves

• Fintech, mortgage, or regulated industry experience.
• Awareness of how AI systems operate in regulated environments and how that impacts product design.
• Familiarity with AWS cloud AI services (Bedrock, SageMaker) or equivalent cloud AI platforms.
• Experience with document AI including classification, extraction, completeness, or verification workflows.
• Hands-on familiarity with modern data warehouses (Snowflake, BigQuery) and transformation tools (dbt).
• Product analytics tools (PostHog, Pendo, Mixpanel) and SQL proficiency.
• Experience scaling a small AI product or engineering function.

What Success Looks Like

• A clear AI product roadmap exists and is being executed against within 90 days.
• AI capabilities embedded in the POS are measurably more accurate, reliable, and cost-efficient within 180 days.
• AI engineers and data engineers are productive, unblocked, and shipping.
• Product, Engineering, and Customer Experience are aligned on what AI can and cannot do, and why.
• Feature intake has a defined process.
• Leadership turns to you for clear, real-time visibility into what’s happening in AI.

Salary Band

$90,000 – $150,000 (depending on experience and location)