Head of Data Science - Document Verification & Biometrics

About the role

We are seeking an exceptional and forward-thinking leader to drive the next generation of AI-powered Document Verification and Biometrics solutions. This role owns the end-to-end data science strategy and execution across document understanding, facial biometrics, and reusable identity systems, powering seamless and secure identity experiences.

What You'll Do

• Own and define the data science vision for Document Verification, Biometrics, and Reusable ID, delivering step-function improvements in accuracy, speed, and user experience.
• Lead and scale a high-performing team of data scientists and applied researchers, setting a high standard for execution, innovation, and accountability.
• Architect and deploy state-of-the-art computer vision and multimodal systems, including vision-language models (VLMs), for document understanding, face matching, liveness detection, and identity verification.
• Drive the development of domain-specific foundation models tailored to identity, documents, and biometrics, leveraging large-scale proprietary datasets.
• Lead the transition to agentic systems, building intelligent agents that can reason over document and biometric signals, automate verification workflows, and adapt dynamically to new fraud patterns.
• Advance fraud and attack detection capabilities, including deepfake detection, presentation attack detection, and counterfeit document detection.
• Design and implement secure and robust systems resilient to emerging threats such as prompt injection and adversarial attacks on multimodal and agent-based systems.
• Leverage vector databases and embedding systems to power similarity search, identity linking, and reusable identity experiences.
• Partner closely with Product, Engineering, and Risk teams to deliver scalable, production-grade solutions that meet both business and regulatory requirements.
• Drive rapid experimentation and deployment, balancing innovation with reliability, explainability, and compliance.
• Own customer communication and stakeholder management, serving as a trusted technical leader in engagements with customers, partners, and internal stakeholders; clearly articulate model behavior, agentic systems, performance trade-offs, and roadmap decisions while building strong, long-term relationships.
• Represent Socure externally as a thought leader in biometrics, document AI, and fraud prevention.

What You Bring

• Advanced degree (MS/PhD preferred) in Computer Science, Electrical Engineering, Machine Learning, or a related field.
• 10+ years of experience in data science, machine learning, or applied AI, with a strong track record of building and deploying production systems.
• Deep expertise in computer vision and multimodal AI, including experience with vision-language models (VLMs).
• Proven experience building domain-specific foundation models or large-scale representation learning systems.
• Strong understanding of biometric systems, including face recognition, liveness detection, and anti-spoofing techniques.
• Experience detecting and mitigating deepfakes, presentation attacks, and counterfeit documents.
• Strong understanding of agentic system design, including agent skills, orchestration/harness frameworks, and real-world deployment of autonomous or human-in-the-loop agents.
• Experience with vector databases and embedding-based retrieval systems.
• Strong awareness of emerging attack vectors, including prompt injection, adversarial inputs, and model exploitation techniques.
• Proven ability to lead and scale high-performing teams while delivering under ambiguity and tight timelines.
• Proficiency in Python and modern ML frameworks (e.g., PyTorch).
• Proven ability to engage with customers and external stakeholders, clearly explaining complex AI systems, trade-offs, and outcomes to both technical and non-technical audiences.
• Experience representing organizations in customer-facing discussions, executive briefings, or public speaking engagements is strongly preferred.

Areas of Innovation

• Agentic Document Verification systems that can reason, adapt, and automate complex verification workflows.
• Deepfake detection at scale, across images, video, and multimodal inputs.
• Injection and adversarial attack detection for multimodal and agent-based systems.
• Agentic red teaming, proactively identifying and mitigating vulnerabilities in AI systems.
• Reusable identity platforms, enabling seamless and secure identity reuse across channels.