AVP Data Science

About LoopMe LoopMe is an AI company solving one of advertising's hardest problems: making brand advertising actually measurable — and making it perform. Our platform runs patented machine learning models across billions of consumer signals in real time, optimising campaigns toward outcomes like purchase intent, brand lift, and foot traffic rather than proxy metrics like clicks. The result is 2–5x better performance than industry benchmarks, at scale. We operate a high-load programmatic infrastructure — processing millions of ad requests per second with sub-200ms response times globally. This isn't a layer on top of someone else's stack; it's built from the ground up, in-house, by the team you'd be joining. Founded in 2012 and headquartered in London, we now have 400+ people across 19 cities and have sustained 40% revenue CAGR since 2018. The engineering problems here are real, the ownership is genuine, and the scale is significant. The opportunity As AVP Data Science, you will work on LoopMe's AI traffic shaping and auction dynamics system and other AI products across the business. This is a senior leadership role: part technical owner, part product owner, part commercial partner. You will work directly with commercial, clients, operations, product and engineering teams, and report progress and impact to senior leadership. The wider Data Science team — led by Chief Data Scientist Dr Leonard Newnham — operates as a single team across London, Poland, and Ukraine, with a track record of publishing award-winning research in automated bidding. This role reports to the Chief Data Scientist. What you'll do Own a defined slice of the AI traffic shaping and auction dynamics system roadmap end to end, covering modelling strategy, release planning, measurement and production performance. Own the full product lifecycle for AI products, from opportunity identification through launch, monitoring and iteration. Partner with commercial teams and clients to understand their needs and coordinate releases with technical and non-technical stakeholders. Partner with operations teams to embed AI products safely and measurably into day-to-day business processes. Establish clear, evidence-led reporting on progress, risks and measurable commercial impact for senior leadership. Develop practical business recommendations by turning ambiguous commercial problems into testable data science questions. Manage technical quality by reviewing model logic, system design and code where needed. What you'll bring Essential Strong commercial experience in data science, machine learning or applied AI, ideally including production systems at significant scale in an adtech environment. A track record of owning AI/ML products end to end, from initial idea through production release and measured business impact Experience leading, coaching or managing data scientists, ML engineers or closely related technical teams. Deep understanding of experimentation, causal…