Top Data Jobs in London

Lead design and evolution of a scalable data platform, build shared ingestion/transformation/orchestration capabilities for batch and real-time, integrate tools (dbt, Spark, Beam), implement data quality/observability/security/testing, enable AI-assisted data engineering, mentor engineers, and influence the data platform roadmap to support analytics and ML at scale.

Lead data strategy for Boots, turning data into actionable audience and bidding strategies, measurement and testing frameworks. Own data partnerships, privacy-first governance, and strategic project delivery. Collaborate with MarTech, CRM and activation teams, guide agentic AI personas, and deliver measurable business impact through audience segmentation, testing and integrated data solutions.

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.

Support ESG data and analytics for private markets and real assets: calculate fund-level ESG metrics, develop automation requirements with tech partners, ensure data governance and calculation rigor, respond to client reporting requests, build dashboards and integrate ESG analysis into investment processes, and maintain subject-matter knowledge of reporting frameworks and best practices.

Lead regional commissioning and technical operations for mission-critical data centres. Provide technical authority, ensure safe and consistent delivery across projects, manage schedules, risks, and quality, engage clients and stakeholders, drive continuous improvement, and promote health, safety, and compliance.

Lead customer-facing data science and AI projects for commodity markets: identify high-impact opportunities, prototype and deploy ML/AI solutions (including LLMs and agentic approaches), measure business impact, collaborate with product, engineering and domain experts, and share learnings to shape ICIS data and AI products.

Build production-grade AI agents, integrations, data pipelines, and infrastructure directly within strategic customer environments. Engineers rapidly navigate unfamiliar codebases and data stacks, own technical delivery through production deployment and handoff, communicate credibly with customer engineering teams, document and train users, and feed recurring engagement insights back to Product and Engineering. The role requires 30–50% travel during active engagements and emphasizes backend systems, agent integrations, real-world deployments, and maintainable customer outcomes.

Leads and scales Redpanda’s EMEA commercial sales team by hiring, coaching, and managing Account Executives. Owns regional revenue targets, pipeline generation, forecasting, sales process development, and quota attainment. Partners with Sales Development, Solutions Engineering, Marketing, and Customer Success to drive mid-market growth. Coaches deal strategy, technical positioning, negotiation, and closing while representing commercial-segment needs in cross-functional planning.

Lead EMEA procurement for CAPEX and large-scale data centre development. Define regional sourcing strategy, run RFI/RFQ/RFPs, negotiate major contracts, establish supplier frameworks, drive cost optimization and governance, report to executives, and build a high-performing regional procurement team.

Own subscription and lifecycle analytics: analyze funnels, build dashboards, perform retention deep-dives, and develop LTV and churn models. Collaborate with Growth, Product, and Editorial to translate insights into product and marketing actions that improve retention and revenue.

Design, build, and maintain scalable data pipelines (Snowflake, Dagster, SQL) to process financial, climate, and ESG data. Integrate climate risk models into production, automate deployments via GitHub/CI-CD, and uphold engineering best practices through testing, documentation, and code reviews while collaborating with data scientists and stakeholders in an agile team.

Design, develop and maintain conceptual, logical and physical enterprise data models. Translate business requirements into scalable data solutions, define source-to-target mappings, support relational and NoSQL implementations, optimise performance, drive data modelling standards, perform reverse engineering, and advise stakeholders on data architecture, governance and integration throughout project lifecycles.

Provide senior-level data engineering and analytics consultancy: capture requirements, design data platform and BI/advanced analytics solutions, implement ETL and data transformation, and deliver clear reports and dashboards using Microsoft and cloud data technologies.

Design, build, and maintain scalable cloud-native data pipelines and lake/lakehouse infrastructure for multi-modal scientific data. Optimize storage layouts and access patterns for analytics and GPU-based ML training, implement versioning, lineage, and quality monitoring, operate workflow orchestration and containerised environments, and collaborate closely with data scientists and ML researchers to enable efficient model training and research workflows while meeting security and governance needs.

Design and build scalable pipelines to ingest, transform and harmonise large healthcare datasets into research-ready formats (OMOP/FHIR). Lead technical direction, implement data quality and governance, collaborate with data scientists and epidemiologists, and deliver reusable data products using cloud and big-data technologies.

Lead development and execution of data strategy plans for clinical programs, support assessment selection and data collection consistency (especially in Neuroscience), perform early data landscaping using internal/external sources, advise on clinical assessments and risks, and provide downstream data oversight to enable data-driven decisions and smooth handoff to trial teams.

Lead the technical implementation of Prime Video's geo holdout incrementality programme across multiple European markets. Build and maintain NUTS3-to-platform geo mappings, weekly campaign matching, API scripting, bulk upload templates, and automated QA/verification reporting. Own automation, platform integrations, onboarding of extension markets, and documentation to ensure experiment integrity and scalable operations. Collaborate with Measurement and Activation teams and present technical findings to stakeholders.

Build, test, optimize, and maintain ELT pipelines supporting analytics, reporting, and data products. Deploy config-driven workflows to Databricks across a multi-tenant environment, extend data models, support CI/CD and ingestion projects, monitor pipeline health, troubleshoot failures, and collaborate with data and product teams to deliver reliable, well-tested data.

Develop and deploy machine learning and statistical models that solve business problems and create measurable impact. Responsibilities include exploratory data analysis, feature engineering, experimentation, model optimization, explainability, MLOps, prompt engineering, and cloud-based data collaboration. The role involves working with clients and stakeholders, communicating insights through visualization and storytelling, and contributing to scalable analytics, Generative AI, and data science solutions.

Lead enterprise-scale data science and AI initiatives, defining lifecycle best practices, architecture, governance, metadata frameworks, and MLOps practices. Design and deploy machine learning, statistical, optimization, and LLM solutions using cloud platforms. Lead multidisciplinary teams, translate business needs into analytical solutions, collaborate with engineers and stakeholders, and communicate insights to technical and executive audiences.

Conduct real-world data analyses using SAS or R and commercial claims and EHR databases. Build longitudinal study cohorts, define baseline and outcome variables, apply epidemiologic methods, perform statistical tests, regression and survival analyses, assess data quality, review protocols and statistical analysis plans, and program tables, figures, and listings. The role requires advanced biostatistics or epidemiology expertise and strong communication skills.

Lead high-complexity data science initiatives improving search ranking, retrieval, relevance, and evaluation. Develop and deploy production machine-learning and NLP solutions, investigate LLM-based ranking features, optimize SQL queries, and work with sparse privacy-preserving data. Define strategies for ambiguous problems, guide other data scientists, collaborate with product teams, and own projects from scoping through production.

Lead delivery and production monitoring of generative AI and data science solutions. Collaborate with product, engineering, and business teams. Build evaluation frameworks for LLM efficacy, dataset quality, and guide product roadmaps. Drive prompt engineering, RAG designs, validation pipelines, and observability for enterprise GenAI deployments.

Lead measurement and analytics for global client accounts: design measurement and experimentation frameworks, deliver campaign reviews and actionable insights, engage senior client and internal stakeholders, manage integrations and automation with data engineering, contribute to commercial proposals and pitches, mentor analysts, and drive data strategy and thought leadership using statistical methods and advanced media measurement tools.

Lead and grow AECOM's Data Centre mechanical team across the UK/EMEA, deliver and quality-assure mechanical design for hyperscale and co-location projects, drive business development, manage project commercials and programme delivery, set technical standards, mentor staff, and ensure client relationship and risk management.