Principal Product Manager, Data

Figure Lending, San Francisco, CA +2

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.

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Principal Product Manager, Data

Figure Lending, San Francisco, CA +2

Posted pay

$154K–$231K

a year

Compared with similar jobs

This base pay is below the median for principal product management jobs that are remote in the US.

Median: $220K a year
Other jobs in the group: 90

We compare the middle of each posted range. This is base pay as posted. The group does not include this job.

Location: San Francisco, CA +2
Work type: Remote
Level: Principal
Experience: 6+ years
Skills in the job text: SQL
Posted: 4 days ago
Last check: 5 h ago
Job type: Product management

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Figure Lending

Open tech jobs: 7
With posted pay: 100%
Median posted base: $132K–$198K

About Figure Lending: funding, hiring and pay

Figure Lending

About Figure Lending: funding, hiring and pay

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The role

What you will 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

Principal Product Manager, Data

The role

Principal Product Manager, Data

The role

What you will do

What you need

Pay and benefits

Salary

Benefits

About Figure Lending

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