About The Position

We are looking for a Senior Product Manager to own critical components of Socure’s Document Verification (DocV) platform, with a focus on the forensic engine, decisioning logic, and computer vision models. This role sits at the intersection of machine learning, fraud detection, and product decisioning, and is responsible for driving the systems that translate signals into outcomes. You will work closely with Data Science and Engineering to improve detection of fraud vectors such as injection attacks, deepfakes, and document manipulation, while also shaping how those signals are operationalized into scalable and configurable decisioning frameworks. This is a highly technical and impact-driven role requiring strong product judgment, deep curiosity about fraud patterns, and the ability to translate complex model behavior into clear product logic and customer-facing outcomes.

Requirements

  • 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.
  • 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.

Nice To Haves

  • 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.

Responsibilities

  • 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.

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What This Job Offers

Job Type

Full-time

Career Level

Mid Level

Education Level

No Education Listed

Number of Employees

101-250 employees

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