Senior Product Manager - Document Verification

Socure Socure · Vertical AI · US · Hybrid · Product

Senior Product Manager for Socure's Document Verification (DocV) platform, focusing on the forensic engine, decisioning logic, and computer vision models. The role involves driving systems that translate ML signals into outcomes, improving detection of fraud vectors like injection attacks and deepfakes, and shaping how signals are operationalized into decisioning frameworks. Requires strong product judgment, curiosity about fraud patterns, and ability to translate complex model behavior into product logic and customer outcomes.

What you'd actually do

  1. Own the roadmap and execution for DocV’s forensic engine, including detection of document fraud, injection attacks, and AI-generated content.
  2. Design and evolve decisioning frameworks that translate model outputs into actionable outcomes.
  3. Work closely with high-value customers to understand fraud patterns, edge cases, and operational needs.
  4. Collaborate with Engineering and Data Science to translate product requirements into technical execution.
  5. Use SQL and analytics tools to evaluate model performance, decisioning outcomes, and conversion impact.

Skills

Required

  • 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.
  • 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.

Nice to have

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

What the JD emphasized

  • forensic engine
  • decisioning logic
  • computer vision models
  • machine learning
  • fraud detection
  • product decisioning
  • model performance
  • fraud patterns
  • emerging attack vectors
  • ML models
  • Computer Vision

Other signals

  • ML models
  • computer vision
  • fraud detection
  • decisioning logic
  • APIs
  • SQL