Senior Product Manager - Document Verification

What You'll Do

Forensic Engine & Detection Strategy

• 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.

Decisioning & Risk Logic

• 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.

Customer-Centric Product Development

• 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.

Cross-Functional Leadership

• 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.

Data & Performance Analysis

• 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.

Go-To-Market & Enablement

• Support product launches and enhancements with clear positioning and documentation.
• Enable internal teams and customers to understand and effectively use decisioning capabilities.

What You Bring

Experience

• 3–5 years in product management, preferably in identity verification, fraud prevention, or other ML-driven products.

Technical Expertise

• Strong understanding of APIs, SQL queries, databases, and product architecture.

Machine Learning Familiarity

• Experience working closely with ML models, including understanding model outputs, evaluation metrics, and tradeoffs.

Computer Vision (Preferred)

• Exposure to image processing, OCR, or document verification systems is a strong plus.

Fraud & Identity Domain Knowledge

• Familiarity with fraud detection techniques, identity verification flows, or risk-based decisioning systems.

Analytical Skills

• Comfortable working with data, writing queries, and deriving insights to inform product decisions.

Product Judgment

• Ability to balance technical complexity, customer needs, and business impact in decision-making.

Customer Focus

• Experience working directly with customers, especially in complex or high-stakes environments.

Communication

• Strong ability to explain complex technical concepts clearly to both technical and non-technical audiences.

Collaboration

• Proven ability to work cross-functionally with Engineering, Data Science, and go-to-market teams.