Principal Product Manager

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

• →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.

• 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.

• 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.
• Located in Pittsburgh or San Francisco with the ability to come into the office to collaborate with other team members.

• 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.

Kiavi takes a market-based approach to pay, and pay may vary depending on your location. The expected annualized base pay range for this full-time role to be performed remotely is $210,000 - $275,000 + benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries. Within the range, the successful candidate’s starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. Your recruiter can share more about the specific base pay range for your preferred location during the hiring process. Please note that the pay range listed above reflects the base salary only, and does not include the target value of bonus or benefits.