Principal Product Manager

Kiavi is hiring a Principal Product Manager to drive its Data XFN portfolio of ML, GenAI, and data products from concept through production. The role partners with Data leadership and cross-functional teams to deliver measurable business outcomes, manage execution and dependencies, and guide product strategy, model delivery, data acquisition, and communication.

Responsibilities:

• Collaborate with Data leadership to operationalize the Data XFN roadmap and track its quarterly OKRs. Tie every major initiative to a key result, every result to a goal, with an owner on each contributing team
• Facilitate data leadership forums as decision meetings, not status updates. Bring the hard calls to the table and set an example by queuing up data for each decision
• Look past the committed roadmap. Find new data and AI opportunities and write them up with size, ROI, risk and a go/no-go recommendation
• Partner with relevant partners to develop program milestones, track progress, handle cross-team dependencies, mitigate risks and run cross-team communications
• Manage the priorities of multiple data science and development teams, ensuring alignment and streamlined execution of multiple concurrent projects
• Write business cases that Finance signs off on before work starts, with conservative and aggressive forecasts
• Measure business outcomes (cost, quality, hours saved) as hard as model accuracy. A live model that doesn't move the business metric isn't a win yet
• Work with FP&A and Operations to turn efficiency gains into approved capacity and budget changes
• Find cost savings in vendor spend and manual work, and deliver them
• Take ML and GenAI work from discovery or prototype through proof of concept, shadow mode, pilot and production
• Work with application engineering teams and product counterparts, to move fast and learn from pilots
• Set up phased QA, human evaluation and feedback loops so operations teams trust a model before it scales
• Guide launches through model governance, legal and compliance review, and user training
• Analyze why a model underperforms and work with Data Science to improve it, or recommend a pivot or stop
• Drive execution across teams by managing dependencies, tracking milestones, removing blockers, and ensuring initiatives actually make it into production
• Align Engineering, Operations, Design and Data on shared definitions, prototypes and a written spec before development starts
• Build alignment with leaders one-on-one ahead of group decisions
• Secure engineering time that wasn't planned by showing the size of the opportunity
• Partner with operations, underwriting, sales, servicing, legal and finance teams
• Proactively remove obstacles to drive momentum and progress. Identify communication gaps and resolve issues in order to move quickly
• Find new datasets the business needs, test their coverage and quality, and make the case to buy
• Take purchases through procurement and own vendor relationships and renewals
• Build prototypes with low-code and AI tools, and write SQL and use AI to answer your own questions
• Go deep enough on APIs, queues and data pipelines to help engineers make architecture decisions
• Use AI coding tools to ship small changes yourself, and push Data, Eng and Product teams to work AI-first
• Share the Data team's work across the company through newsletters, launch notes and product reviews
• Proactively produce regular updates to the business on timelines, outcomes, projections, and explicitly manage expectations when OKRs are at risk
• Mentor PMs on business cases, experimentation and GenAI

Requirements:

• 6+ years of product management, including complex technical or data products built with data scientists and engineers
• A builder's mindset: you turn ideas into working prototypes and proofs of concept that show what's possible before engineering commits
• A record of taking ML or GenAI products from 0 to 1 into production and proving their business impact in dollars or hours
• Experience managing initiatives across teams, building sustainable processes and coordinating release and priorities
• Strong business case skills: sizing, forecasting, ROI, and getting sign-off from Finance and executives
• Ability to lead through influence across a large, decentralized group of teams and strong opinions
• Hands-on technical fluency: SQL, cloud data warehouses, APIs and event-driven systems, and low-code tools (such as Retool, Zapier)
• Daily, practical use of AI tools for writing, analysis, prototyping and code
• Experience with model evaluation, human-in-the-loop QA and production monitoring
• Clear writing and speaking, from executive narrative down to a detailed spec
• Bachelor's degree in Computer Science, Engineering, Data Science, Finance or a related field
• Lending, mortgage or real estate experience, especially valuations, underwriting, title or draws
• Experience buying and integrating third-party data (property, MLS, liens, zoning)
• Experience with outcome-based OKRs at individual, team, department, company levels
• Experience integrating products, data platforms or teams after an acquisition
• An advanced degree