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.
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 RoleThis 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
ResponsibilitiesStrategy 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.
QualificationsRequired- 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.
Preferred- 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.
Benefits and Perks- Medical, Dental, and Vision Insurance: Kiavi offers free employee-only coverage for medical, dental, and vision plans. Dependents receive a heavily discounted premium, competitively placed in the 90th percentile for cost.
- Health Savings Account (HSA): For those enrolled in a High Deductible Health Plan (HDHP), Kiavi provides an annual employer contribution to HSAs: $1,000 for individual coverage and $2,000 for dependents.
- Disability and Life Insurance: Immediate access to short-term and long-term disability insurance, as well as group life and AD&D insurance, with coverage at 1x annual salary.
- Remote Work Policy: Kiavi supports working remotely for day to day activities for most roles and occasionally may ask employees to gather in person for key meetings, training, team building etc. For remote workers, Kiavi offers a one-time office setup reimbursement of up to $500 and a monthly $85 stipend for internet and phone expenses. For in-office workers, Kiavi offers employer contributions to commuter benefits account, either for parking or public transportation.
- Flexible Time Off: Employees are empowered to manage their own time off, with no strict limits, as long as it's approved by their manager.
- Paid Leaves: Up to 8 weeks of paid leave for medical or caregiver purposes, and 12 weeks of paid maternity/paternity leave. Short-term disability leave is also available for birthing mothers.
- 401(k) & Company Match: A 401(k) plan managed by Fidelity, with Kiavi matching 100% of contributions up to 3% of compensation or $8,000 (whichever is less). All employer contributions are immediately vested.
Protect yourself from recruitment scams:- If you are offered an interview or a role with Kiavi, this will be communicated with you through email, via an official email address ending in @kiavi.com or @lendinghome.com.
- You will never be asked for payments of any kind during the process.
- We also never communicate with candidates via Whatsapp at any point during the recruitment process.
- We won't ask for personal information or data via text message. If you have any concerns regarding how genuine a text message is, please contact your recruiter.
- You can find our official Kiavi careers page here. We advise you to check that the page details and website addresses match if you have any concerns.
