About the role
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. As we continue to expand our data network and verification capabilities, we're looking for a Lead Product Manager to own and scale our Data Sources platform—the foundation that powers document ingestion, fraud detection, bank connectivity, and data reliability across Truv products. Role Overview: We are seeking an experienced Lead Product Manager, Data Sources to own the execution of the roadmap, and execution for 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 be responsible for building scalable capabilities that improve data coverage, quality, reliability, and trust across Truv's platform. You will work closely with Engineering, Data Science, Fraud, Operations, Customer Success, and GTM teams to deliver products that directly impact conversion, customer satisfaction, and operational efficiency.
What you'll do day to day: Set the multi-quarter roadmap for Data Sources across docs, fraud, and aggregation; align it with revenue, client commitments, and platform-level constraints. Write tight specs and 1-pagers; partner with Eng and Data Science to scope, build, and ship. Talk to clients (mortgage lenders, government agencies, tenant screeners) weekly; turn their feedback into 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). \n
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 the 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