Location
London, with hybrid working - 3 days a week in our Farringdon office

Hours
Full Time

Salary
Competitive, with bonus

About the Role
LoopMe is an AI company solving one of advertising's hardest problems: making brand advertising actually measurable and performant. 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 infrastructure is built from the ground up in-house by the team you will be joining.

Founded in 2012 and headquartered in London, LoopMe has over 400 people across 19 cities and has sustained 40% revenue CAGR since 2018. The engineering challenges here are real, ownership is genuine, and the scale is significant.

As AVP Data Science, you will own LoopMe's AI throttling system end to end and lead AI products across the business. This senior role combines technical ownership, product ownership, and commercial partnership. You will lead a team of four within LoopMe's 17-person Data Science organisation, working directly with commercial, operations, product, and engineering teams and reporting measurable impact to the Senior Leadership Team. The wider Data Science team operates across London, Poland, and Ukraine and has a track record of publishing award-winning research in automated bidding. This role reports to the Chief Data Scientist.

Your responsibilities include owning the AI throttling system roadmap, modelling strategy, release planning, measurement, and production performance; leading and developing a team of data scientists and ML engineers; managing the full product lifecycle for AI products from opportunity identification through launch, monitoring, and iteration; partnering with commercial and operations teams to embed AI products safely and measurably into business processes; establishing clear, evidence-led reporting on progress, risks, and commercial impact for senior leadership; developing practical business recommendations by translating ambiguous commercial problems into testable data science questions; and managing technical quality through review of model logic, system design, and code as needed.

Experience
- Strong commercial experience in data science, machine learning or applied AI, ideally including production systems at significant scale in an adtech environment
- Proven 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 measurement, model monitoring and practical realities of production ML
- Strong Python, SQL and data engineering literacy with ability to go deep technically when required

About You
- Excellent communication skills, able to explain complex technical systems to commercial, operations and senior leadership teams
- Ability to partner effectively across commercial, product, engineering and operations teams
- Comfortable managing cross-functional delivery across distributed teams and time zones

Qualifications
Nice to have:
- Experience with adtech, real-time bidding, DSPs, SSPs, throttling, pacing or auction systems
- Experience with Google Cloud, Docker, Kafka, Spark, Airflow, ElasticSearch, ClickHouse or similar production infrastructure
- Experience owning product roadmaps, release governance or cross-functional delivery