Top AI & Machine Learning Jobs in London

Lead product direction and build agentic AI systems for autonomous merchandising optimisation: prototype multi-agent flows, take prototypes to production, define trust/governance, drive measurable experiment throughput and conversion uplift, and partner across engineering, data science, and brand teams.

Own analytics for Hotels.com growth areas including customer growth, loyalty, retention, merchandising campaigns, and website performance. Analyze customer and booking data, design A/B and pre/post experiments, apply statistical and causal methods, build dashboards, and translate findings into strategic recommendations. Collaborate with product, marketing, supply, and analytics teams to improve revenue, engagement, retention, and marketplace KPIs while influencing senior stakeholders.

Analyze large, multi-source datasets using SQL and Python, R, or similar tools to generate insights for hotel partnerships and commercial decisions. Build reusable datasets, reporting solutions, visualizations, dashboards, and basic data workflows. Apply descriptive statistics, regression, experimentation, and causal analysis while clearly communicating assumptions and recommendations to technical and non-technical stakeholders. Collaborate with analytics, product, engineering, data, and commercial teams.

Develop and productionize scalable machine learning systems for content understanding, search, ranking, recommendations, moderation, computer vision, and generative AI. Responsibilities include data preparation, feature engineering, model training, evaluation, deployment, monitoring, optimization, batch and real-time pipelines, RAG and LLM applications, system design, observability, and cross-functional technical leadership.

Define and deliver AI-powered analytics and decision-support products. Set strategy and roadmaps, partner with engineering and analytics teams to build LLM/RAG-driven conversational and agentic experiences, translate customer needs into requirements, use data to measure performance, and ensure trust, governance, privacy, and responsible AI practices.