Head of Data Science - Identity & Compliance

Responsibilities

• Own and drive the data science vision for Identity & Compliance, delivering measurable improvements in accuracy, coverage, latency, and customer impact across all products.
• Lead, build, and develop a high-performing team of data scientists and applied researchers, fostering a culture of technical excellence, speed, and accountability.
• Architect and deploy advanced machine learning systems across identity verification, entity resolution, sanctions screening, and compliance risk modeling.
• Drive the development of graph-based intelligence, including large-scale graph neural networks (GNNs) and link analysis models to power Identity Graph, fraud detection, and watchlist matching.
• Lead the design and implementation of agent-based AI systems, enabling automated decisioning, case triage, investigation workflows, and adaptive compliance strategies.
• Advance state-of-the-art modeling approaches, including deep learning, representation learning, graph learning, and multimodal fusion across structured and unstructured data.
• Drive innovation in Identity Graph and Prefill systems, improving identity resolution, linking, and enrichment capabilities across global datasets.
• Partner closely with Product, Engineering, Risk, and Go-to-Market teams to translate business needs into scalable AI solutions.
• Own the end-to-end model lifecycle, including data strategy, feature engineering, model development, evaluation, deployment, and monitoring.
• Ensure alignment with regulatory and compliance requirements, balancing model performance with explainability, auditability, and governance.
• Own customer communication and stakeholder management, serving as a trusted technical leader.
• Represent Socure externally as a thought leader in identity, compliance, and applied AI.

Requirements

• Advanced degree (MS/PhD preferred) in Computer Science, Statistics, Mathematics, Engineering, or a related field.
• 10+ years of experience in data science and machine learning, with a strong track record of delivering production-grade AI systems at scale.
• Significant experience in identity verification, KYC/AML, fraud detection, or risk modeling in fintech or adjacent domains.
• Proven leadership experience managing and scaling high-performing data science teams.
• Deep expertise in modern machine learning techniques, including deep learning, graph neural networks, entity resolution, and large-scale data systems.
• Strong understanding of agentic system design, including agent skills, orchestration/harness frameworks, and real-world deployment of autonomous or human-in-the-loop agents.
• Strong experience working with heterogeneous data sources, including structured data, text, network/graph data, and third-party identity signals.
• Demonstrated ability to drive ambiguous, high-impact problems to production.
• Hands-on experience with Apache Spark and large-scale distributed data systems.
• Proficiency in Python and modern ML frameworks (e.g., PyTorch).
• Proven ability to engage with customers and external stakeholders, explaining complex AI systems.