Lead Product Manager, Data Sources

Truv; Emeryville (CA); On-site; USD 130,000 - 160,000; Full time

Benefits offered by this job

Base salary, bonus and equity package

Job summary

Truv is seeking an experienced Lead Product Manager, Data Sources to drive the execution of the roadmap for data integrity and fraud prevention. This is a pivotal role collaborating across various teams, enhancing document parsing and establishing strong data metrics for improved client services.

With a career encompassing over 7 years in product management focusing on data, you'll play a critical role managing complex systems and supporting Truv's growth in diverse verticals like mortgage and tenant screening. The position offers a base salary alongside bonus and equity opportunities.

Qualifications

• 7+ years in product management, particularly in data and fintech.
• Experience with data-intensive products such as fraud detection.
• Strong problem-solving skills with a customer-centric approach.

Responsibilities

• Set multi-quarter roadmap for data sources.
• Collaborate with engineering and data teams.
• Optimize document parsing and fraud detection.

Skills

Product management

Data analysis

Machine learning

Technical fluency

SLA management

Written communication

Customer obsession

Job description

About Truv

Truv is building the infrastructure layer that powers consumer‑permissioned income, employment, identity, and asset verification. We help lenders, fintechs, and financial institutions make faster, more accurate decisions while delivering a seamless consumer experience.

Role Overview

We are seeking an experienced Lead Product Manager, Data Sources to own the execution of the roadmap and the systems that collect, process, and validate consumer financial data. This role sits at the intersection of data infrastructure, machine learning, fraud prevention, and financial data aggregation. You will build scalable capabilities that improve data coverage, quality, reliability, and trust across Truv's platform, working closely with Engineering, Data Science, Fraud, Operations, Customer Success, and GTM teams.

What you’ll do day to day

• Set the multi‑quarter roadmap for Data Sources across documents, fraud, and aggregation; align it with revenue, client commitments, and platform‑level constraints.
• Write tight specs and 1‑pagers; partner with Engineering and Data Science to scope, build, and ship.
• Talk to clients (mortgage lenders, government agencies, tenant screeners) weekly; turn their feedback into a prioritized roadmap.
• Own the metrics—parse accuracy, fraud catch rate, bank connection success, SLA hit rate—and the rituals that drive them up.
• Lead and mentor PMs and engineers on the team; raise the bar on shipping speed and quality.
• Represent Truv's data sources story externally (sales calls, conferences, partner reviews).

What you’ll own

Document parsing – Drive the roadmap for parsing income and document types. Improve accuracy, reduce manual review, and shorten time‑to‑report. Partner with Data Science on extraction models and with Ops on the human‑in‑the‑loop pipeline.

Scaling document types – Expand the catalog of supported document types beyond the current set. Identify the highest‑impact additions (by use case, client demand, and revenue impact), prioritize the rollout, and own the end‑to‑end product surface from upload to verified output.

Fraud detection – Define and ship the fraud signal layer across data sources—document tampering detection, deposit‑pattern anomalies, identity mismatches, and synthetic income flags. Balance precision and recall against client‑specific risk tolerances, and build the configuration surface for clients to tune.

Optimizing and scaling bank aggregation – Lead Truv's bank aggregation product, connection success rates, refresh cadence, transaction enrichment, and coverage. Reduce drop‑off, improve match rates, and expand FI coverage where it matters most for mortgage and tenant use cases.

SLAs and reliability – Set, instrument, and defend SLAs across every data source: time‑to‑first‑data, time‑to‑completed‑report, refresh success rate, document parse accuracy, and bank connection uptime. Drive the dashboards, alerts, and review cadence that make these visible—and the engineering work that makes them improve.

What we’re looking for

• 7+ years in product management, with significant time in data, ML/AI, fintech, or verification/identity platforms.
• Demonstrated experience owning a data‑intensive product—document understanding, OCR, financial data aggregation, fraud, or similar—at meaningful scale.
• Strong technical fluency. You can reason about extraction models, API tradeoffs, and pipeline architecture without needing to be hand‑held by engineering.
• Track record of driving SLA/reliability outcomes—moving the numbers that matter, not just shipping features.
• Experience working with regulated industries (mortgage, lending, government) is a strong plus. GSE/FCRA/FNMA/FHLMC familiarity particularly.
• Strong written communication. You write the 1‑pagers and specs that align the team.
• Customer obsession. You spend real time with users and let what you learn shape the roadmap.
• Worked on a verification or KYC/KYB product used by lenders.
• Comfortable shipping in a startup environment where ambiguity is high and the bar moves up.

Why this role

• High leverage. Data sources are upstream of every Truv product. Improvements compound across mortgage, tenant screening, government, and any new vertical we enter.
• Mature platform, room to push. Truv ships in production with major mortgage lenders today. Your job is to take a working platform and make it best‑in‑class.
• Real complexity. Document parsing, fraud, and bank aggregation each have multi‑year roadmaps. You’ll operate across all three as one cohesive system.
• Tight team. You’ll work directly with senior leadership, the engineering teams that own these surfaces, and the largest clients in the space.

Truv is an equal opportunity employer. We celebrate diversity and are committed to building a team where you can do the best work of your career.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

Base salary; bonus and equity package are additional.