AVP, Data Science & AI Strategy — London Hybrid Leader

Nasdaq; Greater London; Hybrid; GBP 150,000 - 210,000; Full time

Benefits offered by this job

Hybrid work environment

Equity incentives

Bonus opportunities

Job summary

Nasdaq, based in London, seeks an AVP of Data Science (NTS) to lead AI strategy and execution, shaping a global technology platform for market solutions. The role requires executive presence to influence across borders and deliver measurable impact in a regulated environment.

You will lead AI Engineering, Data Science and Research teams, drive scalable AI capabilities, and ensure governance and risk considerations are embedded throughout production workloads.

Qualifications

• Proven experience leading AI/ML initiatives at scale.
• Ability to communicate complex AI concepts to diverse audiences.
• Track record delivering measurable business impact in regulated environments.

Responsibilities

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

Skills

AI leadership

Executive communication

Strategy development

Cross-functional leadership

Job description

Nasdaq, based in London, seeks an AVP of Data Science (NTS) to lead AI strategy and execution, shaping a global technology platform for market solutions. The role requires executive presence to influence across borders and deliver measurable impact in a regulated environment.

You will lead AI Engineering, Data Science and Research teams, drive scalable AI capabilities, and ensure governance and risk considerations are embedded throughout production workloads.