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Socure

Digital identity verification and fraud prevention

Head of Data Science - Identity Graph

Applied Machine LearningUpdated on Nov 4
Full-time

About the Job

Requirements

  • An advanced degree (master's or PhD preferred) in Computer Science, Statistics, Mathematics, Engineering, or a related field.
  • At least 10 years of experience in data science and machine learning, including a track record of delivering production-grade systems at scale.
  • Team management experience, including leading and scaling high-performing data science teams and hiring, developing, and retaining senior talent.
  • Deep expertise in entity resolution, record linkage, and entity linking on large, noisy, heterogeneous datasets.
  • Strong experience with graph data modeling, ontology design, and graph machine learning, including graph neural networks, link prediction, and community detection.
  • Experience with identity classification or risk problems, such as synthetic identity detection, thin-file or fragmentary identity handling, or fraud modeling.
  • Experience building enrichment or inference models on identity or consumer data, with attention to accuracy, bias, and privacy.
  • Hands-on experience with Apache Spark and large-scale distributed data systems.
  • Proficiency in Python and modern machine-learning frameworks such as PyTorch.
  • Ability to drive ambiguous, high-impact problems to production while balancing speed, rigor, and business outcomes.
  • Ability to explain complex models and trade-offs clearly to technical and non-technical audiences, including customers and executives.

Responsibilities

  • Own and drive the data science vision for the Identity Graph, delivering measurable gains in accuracy, coverage, latency, and customer impact.
  • Lead, build, and develop a high-performing team of data scientists and applied researchers, fostering technical excellence, speed, and accountability.
  • Drive state-of-the-art approaches to resolve records and signals into unique, high-confidence identities at global scale.
  • Advance models that connect identities to graph attributes, devices, addresses, and other entities.
  • Define and evolve the graph schema, relationships, and semantics to keep it consistent, extensible, and useful across products.
  • Build models that classify identities and supporting evidence, including corroborated identities, thin files, personally identifiable information fragments, and synthetic identities.
  • Lead development of derived attributes and inferences, such as lifestyle indicators and marital status, to extend what the graph can reveal about an identity.
  • Apply deep learning, representation learning, and graph learning, including graph neural networks and link prediction, to structured, unstructured, and network data.
  • Own the end-to-end model lifecycle, including data strategy, feature engineering, development, evaluation, deployment, and monitoring.
  • Partner with Product, Engineering, Risk, and Go-to-Market to translate business needs into scalable solutions that deliver customer value.
  • Ensure alignment with regulatory and privacy requirements while balancing model performance with explainability, auditability, and governance.
  • Improve experimentation and execution velocity while maintaining high standards for reliability and impact.
  • Serve as a trusted technical leader with customers, partners, and internal stakeholders, and represent Socure externally as a thought leader in identity and applied artificial intelligence.

Desired Qualifications

  • Significant experience in identity verification, know-your-customer/anti-money-laundering, fraud detection, or adjacent fintech domains is strongly preferred.
  • Familiarity with graph machine-learning frameworks is a plus.

About the Company

Socure

Digital identity verification and fraud prevention

Socure provides digital identity verification and fraud prevention services for financial institutions through a subscription-based platform. It uses machine learning and advanced algorithms to analyze small amounts of customer data to predict fraud risk. A flagship feature, the Sigma Fraud Score, compares transactions against a proprietary database of known fraudulent users and traits to detect suspicious activity. Additional tools like Device Risk verify session authenticity by gathering device and browser signals, supporting seamless and secure customer experiences. Socure differentiates itself with a robust, data-driven fraud scoring system, a suite of identity verification and compliance tools, and continuous platform updates delivered as a service. Its goal is to help banks and other lenders verify customers quickly and accurately while reducing fraud and ensuring regulatory compliance (e.g., Customer Identification Programs under the Patriot Act).

Company size
501-1,000
Company stage
Series F
Total funding
$900.1M
Headquarters
Incline Village-Crystal Bay, Nevada
Founded
2012

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