AVP Data Science

Overview

In this role you own LoopMe’s AI throttling system and lead AI products across the business. You will manage a four-person data science/ML team within LoopMe’s 17-person Data Science org, collaborating with commercial, operations, product and engineering to deliver measurable impact. You drive end-to-end product life cycles, roadmaps and production performance for AI systems at scale. This is a high-ownership role in a fast-moving, cross-functional environment that shapes how LoopMe optimises campaigns using advanced machine learning.

Pay / Benefits

• Hybrid working
• 25 days annual leave
• 1 month work-from-anywhere
• Annual Wellness Day
• Health Shield
• Cycle to work scheme

Responsibilities

• Own the AI throttling system end-to-end, including roadmap, modelling strategy, release planning, measurement and production performance
• Lead and develop a team of four data scientists and ML engineers, prioritising work and upholding technical standards
• Own the full product lifecycle for AI products from opportunity to launch, monitoring and iteration
• Collaborate with commercial teams and clients to capture needs and coordinate releases with technical and non-technical stakeholders
• Partner with operations to embed AI products safely and measurably into daily business processes
• Establish clear, evidence-led reporting on progress, risks and commercial impact for senior leadership
• Translate ambiguous commercial problems into testable data science questions and practical business recommendations
• Review and ensure technical quality of model logic, system design and code where necessary

Key requirements

• Strong commercial experience in data science, ML or applied AI, including production-scale systems in adtech
• Proven track record owning AI/ML products end-to-end and delivering measurable business impact
• Experience leading and coaching data scientists, ML engineers or related teams
• Deep understanding of experimentation, causal measurement, model monitoring and production ML realities
• Strong Python, SQL and data engineering literacy with ability to dive deep technically when needed

• Excellent communication with commercial, operations and leadership teams
• Leadership and mentoring capabilities
• Cross-functional collaboration across distributed teams
• Python
• SQL
• data engineering literacy

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