The ad says“Strong commercial experience in data science, machine learning or applied AI, ideally including production systems at significant scale in an adtech environment”
The ad says“Experience with Google Cloud, Docker, Kafka, Spark, Airflow, ElasticSearch, ClickHouse or similar production infrastructure”
The ad says“Experience with adtech, real-time bidding, DSPs, SSPs, throttling, pacing or auction systems”
The ad says“Deep understanding of experimentation, causal measurement, model monitoring and the practical realities of production ML”
The ad says“Experience with Google Cloud, Docker, Kafka, Spark, Airflow, ElasticSearch, ClickHouse or similar production infrastructure”
The ad says“Strong Python, SQL and data engineering literacy, with the ability to go deep technically when required”
The ad says“Experience with Google Cloud, Docker, Kafka, Spark, Airflow, ElasticSearch, ClickHouse or similar production infrastructure”
The ad says“Experience with adtech, real-time bidding, DSPs, SSPs, throttling, pacing or auction systems”
The ad says“Experience with Google Cloud, Docker, Kafka, Spark, Airflow, ElasticSearch, ClickHouse or similar production infrastructure”
The ad says“Deep understanding of experimentation, causal measurement, model monitoring and the practical realities of production ML”
The ad says“Experience with Google Cloud, Docker, Kafka, Spark, Airflow, ElasticSearch, ClickHouse or similar production infrastructure”
The ad says“Experience with Google Cloud, Docker, Kafka, Spark, Airflow, ElasticSearch, ClickHouse or similar production infrastructure”
The ad says“Deep understanding of experimentation, causal measurement, model monitoring and the practical realities of production ML”
The ad says“Experience with adtech, real-time bidding, DSPs, SSPs, throttling, pacing or auction systems”
The ad says“Deep understanding of experimentation, causal measurement, model monitoring and the practical realities of production ML”
The ad says“Strong Python, SQL and data engineering literacy, with the ability to go deep technically when required”
The ad says“Experience with adtech, real-time bidding, DSPs, SSPs, throttling, pacing or auction systems”
The ad says“Experience with Google Cloud, Docker, Kafka, Spark, Airflow, ElasticSearch, ClickHouse or similar production infrastructure”
The ad says“Strong Python, SQL and data engineering literacy, with the ability to go deep technically when required”
The ad says“Experience with adtech, real-time bidding, DSPs, SSPs, throttling, pacing or auction systems”
The ad says“Experience with adtech, real-time bidding, DSPs, SSPs, throttling, pacing or auction systems”
You own LoopMe’s AI throttling system and lead AI products across the business — a senior role combining technical ownership, product ownership, and commercial partnership. You 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 SLT
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
Own LoopMe’s AI throttling system end to end, covering roadmap, modelling strategy, release planning, measurement and production performance
Lead and develop a team of four data scientists and ML engineers, setting priorities and maintaining high technical standards
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
Strong Python, SQL and data engineering literacy, with the ability to go deep technically when required
Deep understanding of experimentation, causal measurement, model monitoring and the practical realities of production ML
A track record of owning AI/ML products end to end, from initial idea through production release and measured business impact
Strong commercial experience in data science, machine learning or applied AI, ideally including production systems at significant scale in an adtech environment
Experience leading, coaching or managing data scientists, ML engineers or closely related technical teams
Experience with adtech, real-time bidding, DSPs, SSPs, throttling, pacing or auction systems
Excellent communication skills, able to explain complex technical systems to commercial, operations and senior leadership teams
Experience with Google Cloud, Docker, Kafka, Spark, Airflow, ElasticSearch, ClickHouse or similar production infrastructure
Experience owning product roadmaps, release governance or cross-functional delivery across distributed teams and time zones