AVP – Data Science (NTS)

Overview

As AVP of Data Science, you shape Nasdaq’s AI strategy to power Trade Surveillance, aligning AI capabilities with product and business goals. You lead cross-functional teams to deliver scalable, compliant AI solutions that detect market abuse and improve investigations. You’ll influence stakeholders across Product, Engineering, and Sales and communicate AI concepts to clients. This role offers impact at scale within a regulated, global environment and the chance to build a high-performing, innovative team.

Pay / Benefits

• equity (long-term incentives)
• bonus or incentive plans
• hybrid work environment (London)
• career development and structured progression
• competitive rewards package
• well-being support

Responsibilities

• Define and execute the AI strategy and roadmap for Nasdaq Trade Surveillance
• Partner with Product Management and Engineering to translate strategy into scalable AI-enabled capabilities across detection, investigation, and automation
• Act as a senior AI leader across the organization, influencing stakeholders and representing AI to clients
• Lead and scale a multidisciplinary team spanning AI Engineering, Data Science, and Research
• Establish production-grade AI practices including deployment, monitoring, explainability, and governance
• Drive integration of AI into core surveillance workflows to strengthen detection and investigation of market abuse

Key requirements

• Extensive leadership experience in AI, ML, or advanced analytics with success in scaling teams
• Track record of defining and delivering AI strategies at scale in complex enterprises
• Strong executive presence with ability to communicate complex AI concepts to technical and non-technical audiences
• Experience bridging strategic and hands-on work to deliver production-grade solutions
• Deep understanding of governance, compliance, and operating in regulated environments with AI governance or model risk experience
• Exceptional problem-solving abilities in ambiguous environments
• Knowledge of financial markets, trading, or surveillance environments (preferred)
• Familiarity with product development lifecycles and SaaS platforms (preferred)
• Exposure to enterprise AI governance, risk, or regulatory functions (preferred)

• Executive presence
• Strategic thinking
• Cross-functional collaboration
• AI strategy and leadership
• Production-grade AI practices (deployment, monitoring, governance)
• Model governance and risk management

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