Top Data Jobs in London

Turn data into product and business decisions through metric definition, experimentation, causal analysis, deep-dive research, and clear recommendations. Model reliable analytical data in dbt, build scalable dashboards and reporting, partner with data engineering, and influence leaders across Product, Finance, Growth, GTM, and Customer Success. The role also helps establish data-team standards, tooling, and operating practices in an AI-first environment.

Own Beamery’s Data Foundry vision, strategy, roadmap, and commercial model. Define platform capabilities, data contracts, APIs, MCP access, governance, security, and privacy standards. Partner with product, data science, data engineering, integrations, sales, and customer success teams to prioritize delivery, validate customer needs, establish go-to-market plans, and measure business and customer value. Lead development of trusted, explainable AI and data products for enterprise workforce and talent use cases.

Build and operate backend services, APIs, data catalogs, lineage systems, and publishing platforms for Spotify’s data ecosystem. Develop batch and streaming pipelines, manage metadata and schemas, ensure data quality and reliability, and own projects from technical design through production rollout. Participate in on-call support, observability, automation, and cross-team collaboration.

Design and deliver data architectures, cloud platforms, pipelines, integration frameworks, and governance practices for regulated-industry clients. Advise on data strategies, operating models, lakehouses, warehouses, data mesh, and AI-ready ecosystems. Lead transformation engagements, support pre-sales and thought leadership, mentor consultants and engineers, and maintain expertise in emerging data, cloud, analytics, and AI technologies.

Lead Trade Nation’s data and AI strategy, with emphasis on deploying AI into business workflows and enabling measurable adoption. Own the data warehouse’s business-facing gold layer, semantic models, data quality, analytics engineering standards, and self-service analytics. Lead and develop data analysts while partnering with engineering, compliance, and senior stakeholders. The role requires financial services experience, strong SQL and data modelling skills, cloud data platform expertise, and the ability to deliver AI initiatives from concept through production.

Analyze and improve fraud signals, user segmentation, and review processes for a player rewards platform. Investigate suspicious users, monitor segment performance, evaluate verification methods such as facial verification, ID scanning, and phone verification, and recommend third-party fraud tools. Partner with product and data teams to translate analysis into operational fraud controls as fraud patterns evolve.

Leads the design, development, testing, integration, and optimization of scalable data-processing and persistence components. Establishes engineering and operational standards, ensures pipeline performance and compliance, troubleshoots systems, supports proposals, and collaborates with architects, operations, security teams, customers, and managers. Coaches and develops engineers while overseeing code quality, data models, testing, deployment readiness, and technical delivery.

Lead a proprietary data analysis team supporting researchers, traders, and engineers. Coordinate priorities, define projects, ensure data quality, coach analysts, and personally solve complex data problems. Manage collection, tagging, cleaning, documentation, and validation of alternative, fundamental, market, and reference data. Partner with engineers to automate workflows while maintaining human review of AI-driven pipelines. Communicate findings and project status to technical and non-technical stakeholders.

Oversee quality assurance and quality control for CSA construction activities on a major data centre project. Responsibilities include reviewing ITPs and checklists, verifying workmanship and compliance with specifications, managing BIM 360 quality records, tracking defects and as-built documentation, supporting commissioning, and signing off completed works before energisation. The role requires on-site presence in Finland or recurring international travel to Norway on an 11/3 rotation.

Leads research and advisory work for CIOs on data, analytics, and AI strategy, governance, operating models, architecture, value realization, and emerging technologies. Develops thought leadership, market analysis, presentations, and actionable client guidance; evaluates agentic and composite AI, modern data platforms, AI-ready data, organizational maturity, and capability-building needs. Represents Gartner at client meetings and conferences, supports sales, mentors colleagues, and shapes research agendas.

The Senior Marketing Data Scientist will measure and optimize marketing performance using marketing mix modeling, attribution, customer lifetime value analysis, experimentation, and causal inference. Responsibilities include building scalable data pipelines, automating model deployment and monitoring, creating dashboards, analyzing campaigns, and communicating insights to executives. The role requires strong Python and SQL skills, experience with open-source marketing mix models, marketing analytics expertise, and the ability to lead projects independently.

Serves as the primary technical resource for sales and technical liaison to operations. Determines hyperscale customer requirements, designs complex data center and colocation solutions, presents technical concepts, responds to RFPs, and coordinates technical, contractual, and service-order information. The role requires strong knowledge of space, power, cooling, connectivity, and enterprise or hyperscale colocation solutioning, along with excellent communication and presentation skills.

Own finance-focused data science and analysis, including revenue and ARR reconciliation, cohort economics, pricing experiments, forecasting, and scenario modeling. Partner with Analytics Engineering to maintain certified finance KPIs and ensure data quality across billing and revenue systems. Translate ambiguous questions from FP&A, RevOps, and leadership into reusable analyses that support planning, board reporting, pricing, and IPO readiness.

Leads end-to-end development and production deployment of machine learning, generative AI, and agentic solutions for payments and banking. Responsibilities include problem framing, experimentation, evaluation, MLOps, LLMOps, governance, monitoring, scalable cloud architecture, security, and systems integration. The role establishes reusable AI capabilities, partners with cross-functional stakeholders, mentors technical teams, and communicates model outcomes and operational tradeoffs to technical and non-technical audiences.

Lead the design, development, and operation of scalable data pipelines using Databricks and Azure Synapse. Administer Azure data platform services, Databricks workspaces, security controls, networking, and governance. Manage infrastructure as code with Pulumi or similar tools, build CI/CD automation, monitor platform health, resolve incidents, and mentor junior colleagues while maintaining secure and reliable cloud data environments.

Design scalable human-data solutions for frontier AI clients, translating ambiguous model-training and evaluation needs into executable programs. Lead discovery, methodology, task and workflow design, feasibility estimates, expert profiling, quality strategies, and technical architecture in partnership with engineering and project teams. Advise on fine-tuning, RLHF, benchmarks, red teaming, multimodal data, coding tasks, and agent environments while guiding pilots through early delivery and developing reusable best practices.

Advise enterprise customers on sustainability measurement, greenhouse gas accounting, ESG reporting, emissions verification, and carbon and non-carbon footprint methodologies. Lead measurement sessions, configure sustainability programs, optimize customer data, and deliver advisory services using Watershed’s platform. Collaborate with science, engineering, and product teams to develop innovative sustainability measurement solutions. This role is based in London and requires working in the office four days per week.

Lead BNY’s enterprise data privacy program by advising stakeholders, managing privacy incidents and individual rights requests, conducting privacy risk assessments, overseeing regulatory change, and improving privacy governance through AI and OneTrust. Partner across functions to deliver high-quality privacy outcomes in a highly regulated financial services environment.

Own and build GetGround’s data platform and intelligence-layer foundation. Design ingestion pipelines for structured datasets, APIs, documents, and regulatory guidance; establish data quality, provenance, versioning, and reliability standards; and support AI agents and international expansion. Partner with engineering, product, and property experts to create useful data products, make architectural decisions, monitor and recover pipelines, and shape future data-team capabilities.

Develop fraud scores, statistical models, machine learning algorithms, and analytical products using credit bureau, transactional, and alternative data. Apply regression, AI/ML, segmentation, and champion-challenger techniques to fraud prevention solutions. Extract and blend complex datasets, document model development, support validation and deployment, and collaborate with product, compliance, risk, technology, and global analytics teams.

Leads Oracle Data and AI consulting engagements across strategy, architecture, implementation, and deployment. Designs scalable data platforms on OCI, develops AI and generative AI solutions using Oracle services, and provides technical leadership to delivery teams. Works with clients to translate business requirements into solutions, supports architecture and proposals, and leads workstreams, mentoring, and business development activities.

Owns end-to-end go-to-market strategy for Diligent Boards, including positioning, messaging, launches, market intelligence, sales enablement, pricing, packaging, and adoption. Partners with Product, Engineering, Sales, Customer Success, Finance, and leadership to translate complex SaaS, AI, data, and integration capabilities into customer value. Leads customer research, competitive analysis, AI product marketing, commercial execution, and KPI-driven optimization across SMB and mid-market segments.

Design and scale advanced analytics, statistical, machine learning, and AI solutions for Life Sciences customers across Europe. Integrate Veeva Data Cloud with healthcare datasets, build HCP precision-engagement models, translate technical insights into business value, collaborate with cross-functional teams, and document methodologies, architectures, and validation processes.

Leads Hyve’s enterprise Data and AI strategy, platform, semantic model, governance, analytics, reporting, self-service, and responsible AI adoption. Manages an 8–10-person multidisciplinary team across data engineering, analytics, and AI; rationalizes reporting; establishes scalable cloud data architecture; delivers generative and agentic AI use cases; and connects data products to commercial value. The role also oversees roadmap planning, budgets, partners, executive stakeholder engagement, organizational change, data literacy, and operational governance.

Develop and maintain production data science models for attribution, marketing mix modelling, incrementality testing, and brand impact measurement. Lead projects end to end, select modelling approaches, debug unfamiliar codebases, apply AI tooling, collaborate with QA, communicate technical findings to clients, and mentor junior colleagues. The role requires strong Python, SQL, machine learning, production engineering, stakeholder communication, and independent ownership.