Head of Data Science

Position Overview

Our client is seeking an accomplished Head of Data Science to define and lead the data science strategy across document verification, biometrics, and reusable identity solutions. This executive-level technical leader will oversee the evolution of advanced identity verification capabilities, including the transition from traditional computer vision approaches to foundation model-driven and agentic AI architectures.

The Head of Data Science will lead a high-performing team of data scientists and applied researchers while remaining deeply engaged in technical strategy, architecture, research direction, and production deployment. The role will focus on solving complex challenges involving identity fraud, deepfakes, presentation attacks, counterfeit documents, and adversarial threats at scale.

The ideal candidate combines deep expertise in computer vision and multimodal AI with proven experience building production-grade machine learning systems and leading highly technical teams.

Key Responsibilities

Data Science Strategy & Leadership

• Define and execute the overarching data science strategy across Document Verification, Biometrics, and Reusable Identity products.
• Establish the technical vision, research priorities, and long-term roadmap for machine learning and applied AI capabilities.
• Lead, mentor, and scale a high-performing team of data scientists, machine learning engineers, and applied researchers.
• Establish a culture of technical excellence, experimentation, innovation, and measurable business impact.
• Balance long-term research initiatives with the delivery of production-ready solutions.
• Identify emerging technologies and research trends that can create competitive advantages in identity verification and fraud prevention.
• Develop organizational capabilities, technical standards, and best practices for applied AI development.

Computer Vision & Multimodal AI

• Architect and deploy state-of-the-art computer vision and multimodal AI systems.
• Lead the development and application of vision-language models (VLMs) and related foundation model architectures.
• Drive the evolution of traditional computer vision systems toward modern foundation model-based approaches.
• Develop domain-specific foundation models and large-scale representation learning systems using proprietary identity datasets.
• Establish scalable approaches for training, evaluating, deploying, and continuously improving advanced AI models.
• Translate cutting-edge research into reliable, production-grade identity verification capabilities.

Agentic AI & Identity Verification

• Lead the development and deployment of agentic verification systems capable of reasoning across document, biometric, and identity signals.
• Design architectures that combine multiple sources of evidence to improve verification accuracy and decision-making.
• Establish appropriate evaluation frameworks for agentic and multimodal systems.
• Ensure AI systems are robust, explainable, scalable, and appropriate for high-impact identity verification use cases.
• Guide the transition from deterministic verification workflows toward intelligent, adaptive systems.

Fraud Detection & Adversarial Defense

• Advance machine learning capabilities for detecting and preventing sophisticated identity fraud.
• Develop solutions to identify and mitigate deepfakes, presentation attacks, spoofing attempts, counterfeit documents, and other fraudulent behaviors.
• Lead research into emerging attack vectors and methods used to circumvent identity verification systems.
• Design resilient AI systems capable of operating effectively in adversarial environments.
• Establish strategies for identifying and mitigating prompt injection, model exploitation, adversarial manipulation, and other AI-specific security threats.
• Continuously evaluate model performance against evolving fraud techniques and attack patterns.

Product & Cross-Functional Collaboration

• Partner closely with Product, Engineering, Risk, Security, Compliance, and other stakeholders to translate technical capabilities into customer-facing solutions.
• Guide the development of production-grade, scalable, reliable, and compliant machine learning products.
• Communicate complex technical concepts and tradeoffs clearly to executive and non-technical stakeholders.
• Ensure data science initiatives align with product strategy, customer requirements, business objectives, and risk considerations.
• Collaborate across teams to establish appropriate model governance, monitoring, testing, and deployment practices.

External Thought Leadership

• Represent the organization externally as a technical thought leader in biometrics, document intelligence, computer vision, identity verification, and fraud prevention.
• Participate in industry conferences, executive briefings, panels, research discussions, and other external forums.
• Build relationships with researchers, technology leaders, industry organizations, and academic institutions.
• Contribute to the broader technical community through publications, presentations, patents, or other thought leadership activities.

Environment & Work Style

This is a highly technical leadership position operating at the intersection of artificial intelligence, computer vision, biometrics, identity, and fraud prevention.

The successful candidate will be comfortable operating at both the strategic and technical levels—setting organizational direction while maintaining sufficient technical depth to evaluate architectures, research approaches, model performance, and production implementation.

The role requires a leader who can move rapidly from research and experimentation to scalable production deployment while maintaining strong standards for reliability, security, compliance, and responsible AI.

Essential Qualifications

• Master's or PhD in Computer Science, Electrical Engineering, Machine Learning, Artificial Intelligence, or a related technical field.
• 10+ years of experience in data science, machine learning, applied AI, or a closely related discipline.
• Proven track record of developing and deploying machine learning systems into production.
• Deep expertise in computer vision and multimodal AI.
• Hands-on experience with vision-language models and modern multimodal architectures.
• Experience developing domain-specific foundation models or large-scale representation learning systems.
• Hands-on experience with biometric technologies, including:
• Face recognition
• Liveness detection
• Anti-spoofing
• Direct experience addressing deepfakes, presentation attacks, counterfeit documents, or comparable fraud threats.
• Experience designing and deploying agentic AI systems in production environments.
• Strong proficiency in Python and PyTorch.
• Demonstrated ability to build, lead, and scale a high-performing data science organization.
• Strong technical communication and executive leadership skills.

Preferred Qualifications

• Experience with vector databases and embedding-based retrieval technologies such as FAISS or Milvus.
• Familiarity with adversarial machine learning and AI security threats, including prompt injection and model exploitation.
• Experience developing AI systems in highly regulated or privacy-sensitive environments.
• Knowledge of regulatory frameworks such as GDPR, CCPA, or the EU AI Act.
• Experience representing an organization at industry conferences, executive briefings, technical forums, or public events.
• Publications, patents, or other demonstrated contributions in computer vision, biometrics, machine learning, fraud detection, or related fields.
• Experience working with large-scale proprietary datasets and distributed model training.
• Experience taking research concepts from prototype through production deployment