Principal Product Manager, Data

About Figure

Figure Technology Solutions, Inc. (Nasdaq: FIGR; OPEN: FGRS) is the leading blockchain-native capital marketplace for the origination, funding, sale and trading of tokenized assets. More than 480 partners use its loan origination system and capital marketplace. Collectively, Figure and its partners have originated over $30 billion of loans to date, among other products. The fastest growing components are Figure Connect, its credit marketplace, and Democratized Prime, Figure’s on-chain lend-borrow marketplace. Figure’s ecosystem also includes DART (Digital Asset Registry Technology) for asset custody and lien perfection, and $YLDS, a yield-bearing stablecoin registered with the SEC as a security under the Securities Act of 1933.

Figure is the market leader in real-world asset (RWA) tokenization. The company has received triple-A ratings ratings from S&P and Moody’s on multiple loan securitizations, the first of its kind for blockchain finance.

We’re proud to be recognized as one of Forbes’ Top 50 Fintech Companies and CNBC’s World’s Top Fintech Companies.

About the Role

We’re looking for a Principal Product Manager, Data to join our growing team! As Principal Product Manager, Data, you’ll partner with Data leadership to drive ML, GenAI, and data products from concept to production, ensuring cross-functional execution and measurable business outcomes. The Data cross-functional (XFN) team brings together Data Science, Risk Analytics, MLOps, AI and Data Engineering, and Data Analytics to build the models, datasets, and tools behind automations, valuations, document review, draw disbursements, and risk decisions. You’ll drive a portfolio of products and answer for what they save or earn, not just what they launch. Because the Data team often relies on other teams to get products in front of customers, coordination, communication, planning, and execution are essential. You’ll go deep enough to understand architecture calls with engineers, and you’ll build working prototypes yourself with AI tools.

What You’ll Do

• Drive a portfolio that spans ML and 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; and data platform priorities for MLOps, Data Engineering, and Analytics
• Collaborate with Data leadership to operationalize the Data XFN roadmap and track its quarterly OKRs, tying 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, bringing the hard calls to the table and queuing up data for each decision
• Look past the committed roadmap to find new data and AI opportunities, and write them up with size, ROI, risk, and a go/no-go recommendation
• Partner with relevant teams 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 rigorously as model accuracy, since 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 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 unplanned engineering time 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 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, Engineering, 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, and projections, and explicitly manage expectations when OKRs are at risk
• Mentor PMs on business cases, experimentation, and GenAI
• In your first 6 to 12 months, you’ll:
  • Establish a strong partnership with Data leadership on the Data XFN roadmap and OKRs so leaders across the business know where the team is headed and why
    • Raise automation rates and show measurable cost or time savings for models already in production
    • Get at least one efficiency gain confirmed by Finance and reflected in the budget
    • Move models in early testing to production or to a clear go/no-go decision
    • Lower vendor and data spend without losing coverage or quality
    • Take one new AI or data opportunity from idea to a funded plan

What We Look For

• 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 releases 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 and 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 (preferred)
• Experience buying and integrating third-party data (property, MLS, liens, zoning) (preferred)
• Experience with outcome-based OKRs at individual, team, department, and company levels (preferred)
• Experience integrating products, data platforms, or teams after an acquisition (preferred)
• Advanced degree (preferred)
• Adhere to all company security policies and data handling procedures.
• Complete mandatory security awareness training within required timeframes.
• Promptly report any suspected security incidents or suspicious activity to the Security team.

Salary

• Base Compensation Range: $154,000 - $231,000/yr
• 25% annual bonus target, paid quarterly
• Company equity in the form of RSUs

This is the compensation range for the role in the United States. Actual compensation may vary based on a candidate’s experience, skills, location, internal equity, and evolving business needs. While most offers are generally made within the middle of the range, final compensation is determined based on the factors above.

Benefits

• Comprehensive medical, dental, and vision coverage, with 100% employer-paid premiums for employees and their dependents on select plans
• Company HSA, FSA, Dependent Care FSA, 401(k), and commuter benefits
• Employer-paid life and disability insurance
• 11 observed holidays and PTO plan
• Up to 12 weeks of paid family leave
• Continuing education reimbursement

Depending on your residential location certain laws might regulate the way Figure manages applicant data. California Residents, please review our California Employee and General Workforce Privacy Notice for further information. By submitting your application, you are agreeing and acknowledging that you have read and understand the above notice.

Figure will not sponsor work visas for this position. In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire.

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