Principal Product Manager, Data

Kiavi is one of the nation’s largest private lenders to residential real estate investors (REIs). We harness the power of data & technology to offer our customers a simpler, more reliable, and faster way to access the capital they need to scale their businesses. Formerly known as LendingHome, we’re committed to helping REIs revitalize the approximately $25 trillion worth of aged U.S. housing stock to provide move-in ready homes and rental housing for millions of Americans across the country. Combining our technology and industry expertise, Kiavi has grown to a team of over 500 and has won many awards over the years including Forbes Fintech 50, Finovate Awards, Best Digital Mortgage Platform, Fast Company’s Most Innovative Companies, and many more . Loans Funded: $30 billion in loans funded Homes financed: 100,000+ projects funded for our customers across the country Geography: 49 states + D.C. where we lend to REIs so they can revitalize neighborhoods Employees: Over 500 employees with competitive benefits and perks This position can be based remotely in any of our approved hiring regions. Our hybrid distributed workforce philosophy allows us to find the best talent in the country and build diverse teams, while allowing our colleagues to work from the location that works best for them. Identity Verification Notice: To ensure the security and integrity of our hiring process, all candidates are required to complete a secure identity verification. This process uses biometric and device validation to confirm identity and liveness. All data is handled securely in accordance with our privacy policy. About Us Kiavi and Figure have joined forces to build a premier mortgage marketplace that automates and streamlines financial services for businesses and consumers. Driven by advanced data and technology, we are making the process of funding the next deal simpler, faster, and significantly more reliable. The Data cross-functional (XFN) team is how we do that. It brings together Data Science, Risk Analytics, MLOps, AI/Data Engineering and Data Analytics. Together these teams build the models, datasets and tools behind automations, valuations, document review, draws disbursements and risk decisions. We are hiring a Principal Product Manager to partner with Data leadership to connect business needs to what we build and ensure we deliver measurable outcomes. The Role This Principal Data PM partners with Data leadership to drive ML, GenAI, and data products from concept to production, ensuring cross-functional execution and measurable business outcomes. You drive the execution of a portfolio of ML, GenAI and data products, and you answer for what they save or earn, not just what they launch. The Data team often relies on other teams to get products in front of customers, so coordination, communication, planning and execution are paramount. You act as a strong partner to Data leadership, driving execution with the Data leaders driving organization's technical direction or team priorities. You win priority with business cases, and you align leaders before the big meeting, not during it. You go deep enough to understand architecture calls with engineers, and you build working prototypes yourself with AI tools. The portfolio spans: ML & AI models that automate lending decisions across valuation, underwriting and construction draws GenAI document understanding that classifies, extracts and reviews loan documents Internal tools that put data in front of Risk, Operations and Sales teams Third-party data partnerships, such as property, listings, lien and zoning data Data platform priorities for MLOps, Data Engineering and Analytics Responsibilities Strategy and planning 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 Business cases and value realization 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. Model and AI product delivery 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. Program Management & Cross-functional alignment 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. Data acquisition and vendors 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. Hands-on building 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. Communication and leadership 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. What success looks like in the first 6 to 12 months You have established a strong partnership with Data leadership on the Data XFN roadmap and OKRs, and leaders across the business know where the team is headed and why. Models already in production raise their automation rates and show measurable cost or time savings. At least one efficiency gain is confirmed by Finance and reflected in the budget. Models in early testing move to production or get a clear go/no-go decision. Vendor and data spend is lower without losing coverage or quality. You have taken one new AI or data opportunity from idea to a funded plan. Qualifications Required 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