Top Data Science Jobs in London

Lead the data science function at Fresha, building credibility and infrastructure, shipping impactful ML products, and scaling the team to align with business needs.

Manage high-impact marketing effectiveness programmes, oversee project governance, mentor teams, and drive strategic client insights, focusing on business growth.

The Managing Consultant will lead data science initiatives, drive business growth in financial services, and build client relationships while managing teams and delivering high-impact solutions.

Manage the applied methods Data Science team supporting autonomous driving. Set priorities and roadmaps, review technical designs, ensure statistical rigor, manage delivery, hire and develop data scientists, scale tooling and automation, and build strong relationships with Autonomy stakeholders and customers. The role requires causal inference expertise and experience leading high-performing teams, with desirable experience in ML, simulation, offline evaluation, distributed computing, and productionizing research.

Provide senior advisory and technical guidance for a foundational enterprise data program in financial services. Govern data mart modeling, schema and semantic-layer decisions; establish data science, reproducibility, documentation and data quality standards; assess analytical readiness; sequence remediation; advise on AI governance; and upskill FP&A and data engineering teams.

10-week on-site Data Science internship in London supporting data discovery, enrichment and analysis for quantitative research. Tasks include data blending, statistical analysis, exploratory data analysis, visualization, building tooling, and collaborating with data scientists to scale and automate data processes.

The Director of Colleague Data Science & Analytics will lead the global HR analytics function, driving innovative solutions to support decision-making and enhance the colleague experience.

Design and deliver statistical, probabilistic and simulation models to support decision-making. Translate business questions into deployable analytical solutions, quantify uncertainty, validate and monitor models, and partner with engineering, product and business teams to productionise models and define operational requirements.

Lead and build Alto's data science capability: define roadmap, develop and productionise predictive models (churn, propensity, forecasting), run experiments, create segmentation and scoring, surface insights via ThoughtSpot, collaborate with engineering and commercial teams, and hire/mentor a data science team.

Lead and grow a data science practice for energy and utilities clients: build and coach teams, design and scale AI/advanced analytics products, lead business development, manage high-value programmes, engage senior stakeholders, and contribute market-facing thought leadership.

Lead and deliver end-to-end marketing analytics and MMM projects for global clients. Translate econometric and MMM insights into actionable business recommendations, manage client relationships, and lead teams (3–8 consultants). Support project planning, design, and implementation while communicating results to technical and non-technical stakeholders.

The Data Science Manager will collaborate with business partners, oversee predictive model building, manage a team, and deliver analytical insights to drive business decisions.

Lead and own LoopMe's AI throttling system and related AI products end-to-end. Manage a team of four data scientists/ML engineers, define roadmap, partner with commercial and operations teams, deploy and monitor production models, measure causal business impact, and maintain technical quality across modelling and systems.

Lead the data science organization to deliver AI products, mentoring teams and executing a strategic vision for machine learning applications.

Manage curriculum development for data science and engineering, collaborate with instructors, create content, and drive improvements based on learner feedback.

As a Curriculum Manager, you will manage content development for the data science and engineering curriculum, collaborating with instructors and teams to enhance learning experiences.

Lead and grow a data science team to design, develop, evaluate, and deploy production-grade AI/ML solutions (LLMs, Generative AI, RAG, transformers). Own technical vision, MLOps/LLMOps, model governance, and cross-functional execution with Product and Engineering to deliver scalable business impact in financial markets.

Productionize and support analytics and AI solutions by building reliable Python-based data workflows, deploying containerized/cloud services, modernizing legacy analytics, and collaborating with data science, engineering, and business teams. Provide EMEA support, create documentation and runbooks, and maintain Power BI dashboards and recurring analytical workflows.

Define and lead AI and data science strategy across ML, NLP, search, recommendation, experimentation, and generative AI. Lead and develop teams to deliver production-ready AI systems (LLMs, RAG, semantic search, embeddings), establish evaluation and responsible AI frameworks, and influence cross-functional stakeholders to align roadmaps and drive measurable product and business outcomes.

Lead portfolios of marketing-mix-modeling (MMM) and marketing effectiveness programs, manage teams, ensure delivery quality, translate analytics into C-suite recommendations, drive account growth, and develop talent and practice thought leadership.

Develop and refine NLP projects by improving LLM models, implementing production models, and conducting performance experiments.

As a Data Scientist at QRT, you will work with data to inform trading decisions, prototype data handling code, and solve data related problems.

The Product Data Science Manager leads a team to deliver custom data insights, ensuring analytical excellence and client impact while collaborating with various stakeholders.

The Data Scientist will lead client projects, utilize data science techniques, and manage stakeholder relationships while developing scalable models. Strong communication and problem-solving skills are essential, focusing on innovation and collaboration.

As a Curriculum Manager, you will manage the content development lifecycle, collaborate with instructors, and improve data science curriculum, ensuring technical accuracy and pedagogical effectiveness.