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Socure is building identity trust infrastructure for the digital economy, verifying identities in real time and preventing fraud. As Head of Data Science for Document Verification & Biometrics, you will own the end-to-end data science strategy and execution across document understanding, facial biometrics, and reusable identity systems.
You will lead the evolution from traditional computer vision to foundation model-driven, agentic architectures that tackle sophisticated threats including deepfakes, presentation attacks, and counterfeit documents. This is a leadership role requiring strong ownership, decisive action, and a commitment to pushing the boundaries of applied AI.
Key responsibilities include: defining the data science vision for document verification, biometrics, and reusable ID to deliver step-function improvements in accuracy, speed, and user experience; leading and scaling a high-performing team of data scientists and applied researchers; architecting and deploying state-of-the-art computer vision and multimodal systems including vision-language models for document understanding, face matching, liveness detection, and identity verification; driving development of domain-specific foundation models leveraging large-scale proprietary datasets; leading the transition to agentic systems that reason over document and biometric signals and adapt to new fraud patterns; advancing fraud detection capabilities including deepfake, presentation attack, and counterfeit document detection; designing secure systems resilient to prompt injection and adversarial attacks; leveraging vector databases for similarity search and identity linking; partnering with Product, Engineering, and Risk teams to deliver production-grade solutions meeting business and regulatory requirements; driving rapid experimentation and deployment balancing innovation with reliability and compliance; owning customer communication and stakeholder management as a trusted technical leader; and representing Socure externally as a thought leader in biometrics and fraud prevention.
Required qualifications: MS/PhD in Computer Science, Electrical Engineering, Machine Learning, or related field; 10+ years in data science, machine learning, or applied AI with strong track record of building and deploying production systems; deep expertise in computer vision and multimodal AI including vision-language models; 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; experience detecting and mitigating deepfakes, presentation attacks, and counterfeit documents; understanding of agentic system design including agent skills and orchestration frameworks; experience with vector databases and embedding-based retrieval; awareness of emerging attack vectors including prompt injection and adversarial inputs; proven ability to lead and scale high-performing teams under ambiguity; proficiency in Python and modern ML frameworks like PyTorch; and ability to engage with customers and stakeholders, explaining complex AI systems to technical and non-technical audiences.
