Own the roadmap and execution for DocV’s forensic engine, including detection of document fraud, injection attacks, and AI-generated content.
Support efforts to scale DocV adoption globally, including in the public sector, financial services, and emerging markets.
Partner with Data Science to define, evaluate, and improve model performance across key fraud vectors.
Identify gaps in detection coverage and drive new signal development across image, video, and device layers.
Design and evolve decisioning frameworks that translate model outputs into actionable outcomes.
Build scalable, configurable logic that supports diverse customer risk profiles and use cases.
Balance fraud detection performance with user experience and conversion impact.
Work closely with high-value customers to understand fraud patterns, edge cases, and operational needs.
Translate customer feedback into product improvements and prioritization decisions.
Support complex customer implementations and act as a subject matter expert in DocV decisioning.
Collaborate with Engineering and Data Science to translate product requirements into technical execution.
Partner with the Fraud Investigation team, Customer Success, and Sales to align on product behavior and outcomes.
Drive alignment on tradeoffs between detection accuracy, false positives, and business impact.
Use SQL and analytics tools to evaluate model performance, decisioning outcomes, and conversion impact.
Define and track key metrics related to fraud detection, model precision/recall, and user experience.
Conduct deep dives into fraud patterns and emerging attack vectors.
Support product launches and enhancements with clear positioning and documentation.
Enable internal teams and customers to understand and effectively use decisioning capabilities.

Requirements

3–5 years in product management, preferably in identity verification, fraud prevention, or other ML-driven products.
Strong understanding of APIs, SQL queries, databases, and product architecture.
Experience working closely with ML models, including understanding model outputs, evaluation metrics, and tradeoffs.
Exposure to image processing, OCR, or document verification systems is a strong plus.
Familiarity with fraud detection techniques, identity verification flows, or risk-based decisioning systems.
Comfortable working with data, writing queries, and deriving insights to inform product decisions.
Ability to balance technical complexity, customer needs, and business impact in decision-making.
Experience working directly with customers, especially in complex or high-stakes environments.
Strong ability to explain complex technical concepts clearly to both technical and non-technical audiences.
Proven ability to work cross-functionally with Engineering, Data Science, and go-to-market teams.

Benefits

Offers Equity
Offers Bonus

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

SQLAPIsimage processingOCRfraud detection techniquesrisk-based decisioning systemsmodel evaluation metricsdata analysisproduct architecturemachine learning

Soft Skills

customer engagementcross-functional collaborationtechnical communicationdecision-makingproblem-solvingadaptabilitycustomer needs assessmentproduct prioritizationanalytical thinkingstakeholder alignment