Top Data Jobs in London

Leads MONY Group’s Data Product and Data Analytics teams, setting strategy, roadmaps, priorities, and operating models. Partners with senior stakeholders across the business to deliver trusted, scalable data products, analytics, automation, reporting, and self-service insight. Develops leaders, builds high-performing teams, supports executive and board reporting, and drives engagement, capability, and change across the Data function.

Designs and maintains conceptual, logical, and physical data models supporting data warehouses, data products, reporting, and operational decision-making. The role develops Data Vault methodology, models Salesforce and operational data, defines standards, reviews modelling artefacts, and translates business requirements for Data Engineering teams. It requires collaboration with architecture, governance, security, analysts, engineers, and business stakeholders, plus practical SQL and strong knowledge of dimensional, entity-relationship, and normalization techniques.

Leads upstream AI safety and evaluation initiatives for Frontier Labs, directing project teams, designing scientific experiments, architecting software infrastructure, mentoring data scientists, and advising senior clients. The role combines quantitative research, rigorous methodology, technical leadership, commercial scoping, and thought leadership through novel evaluation methods, speaking, and open-source research.

Leads clinical data management for global CAR T clinical trials, overseeing vendors, data standards, eCRF development, data cleaning, database lock, regulatory submissions, inspection readiness, documentation, timelines, budgets, and quality. Acts as a cross-functional clinical study lead, manages data management deliverables, establishes data conventions, supports vendor selection, contributes to process improvements, and trains study teams and sites.

The Associate Sales Director develops sales strategies and leads a team to enhance business growth in environmental data sales, focusing on client relationships and proposals.

Develops and improves clinical site payment forecasting models, integrating enrollment, operational, and Medidata Payments data. Produces client-ready financial forecasts and reports, analyzes budget utilization and forecast variances, supports revenue recognition, audit compliance, and final payment reconciliations, and collaborates with Clinical Operations, Revenue Recognition, Project Analysis, and Clinical Payments teams across the study lifecycle.

Build, deploy, monitor, and improve supervised machine learning models for credit risk and lending decisions. Own automated credit models, analyze portfolio performance, develop models for new products such as invoice financing, and contribute to risk monitoring and governance. Collaborate with product, risk, and leadership teams, communicate technical decisions clearly, and develop data pipelines and modelling practices in a small, autonomous team.

Graduate Analyst rotating across Architecture, Engineering and DevOps, Data Science, and AI. The role involves designing, developing, and supporting technology solutions; creating software and high-level technical designs; collaborating with multidisciplinary teams; supporting AI-enabled and data-driven client solutions; and working with testing, DevOps, and service management specialists on automated releases. The programme includes mentoring, technical training, client projects, and exposure to cloud platforms, software engineering, and modern data practices.

Lead B2B product marketing strategy for DataCamp’s data and AI SaaS offerings. Responsibilities include developing positioning and messaging, executing go-to-market campaigns, creating sales and marketing collateral, gathering customer insights, managing analyst relations, and conducting competitive analysis. The role partners closely with sales, customer success, product, and content teams to drive revenue, adoption, and market impact.

Leads a global AI and data science organization focused on workforce analytics. Sets technical strategy, directs research from hypothesis through production, develops model and evaluation factories, advances responsible agentic AI, and translates models into business and employee insights. Partners with technology teams on scalable platforms, prioritizes investments, communicates with senior leaders, and ensures compliance in a regulated enterprise environment.

Leads enterprise consulting engagements focused on Salesforce Marketing Cloud and Data 360 architecture. Advises C-level customers, designs customer data and engagement solutions, oversees workshops, delivery, testing, deployment, governance, integrations, consent architecture, and technical standards. Provides thought leadership, manages risks and stakeholders, reviews program artifacts, and drives AI-first consulting methodologies. The role requires extensive Salesforce consulting, people leadership, large-scale data program delivery, and willingness to travel to client sites.

Source, map, evaluate, and promote alternative datasets for trading research. Responsibilities include identifying vendors, tracking market trends, vetting dataset quality and use cases, writing vendor summaries, maintaining data pipelines and catalogs, representing Jump with vendors, and communicating emerging opportunities. The role requires alternative-data market knowledge, vendor contacts, Excel proficiency, familiarity with privacy and compliance issues, and strong organization and writing skills.

Develop and deliver machine learning models (churn, propensity, CLV, recommendations) using large-scale behavioral and transactional data. Translate business problems into modelling solutions, collaborate with engineering, product, and commercial teams, communicate insights to stakeholders, and explore advanced methods (deep learning, sequence models) to drive player engagement and value.

Leads EDF Trading’s enterprise data strategy, architecture, operating model, and multi-year roadmap. Oversees data product management, market data systems, data engineering, governance, quality, and AI enablement. Establishes ownership, controls, quality standards, and investment priorities across data platforms. Builds partnerships across trading, risk, finance, and operations; manages vendors and market data providers; develops multidisciplinary teams; and drives reliable, reusable, governed data products at enterprise scale.

Lead the development and technical direction of high-volume streaming and batch data pipelines for PlayStation game analytics. Responsibilities include building resilient pipeline services, integrating data collection and BI tools, improving data quality, lineage and observability, supporting existing tooling, collaborating with DevOps and analytics teams, and mentoring colleagues. The role uses Go or Python and AWS data services including S3, Redshift, Kinesis, Firehose, Athena and Glue.

Own the strategy and roadmap for Spotify’s data catalogue, metadata, and data lineage products. Identify user and business needs, prioritize platform investments, evaluate build-versus-buy solutions, define success metrics, and collaborate with engineering, design, data science, and product teams. The role focuses on improving data discoverability, governance, trust, and usability for data professionals and AI-driven workflows.

Leads the EMEA mechanical engineering team responsible for data center design, construction support, commissioning, quality assurance, vendor management, and technical standards. Oversees engineering projects, resource allocation, risk management, stakeholder coordination, due diligence, and continuous improvement. Develops team members, supports customer and sales engagements, and ensures critical infrastructure projects meet applicable codes, specifications, operational requirements, budgets, and schedules. Frequent travel across EMEA is required.

Own the end-to-end delivery of applied machine learning products, from framing customer problems and building data pipelines to deploying, monitoring, and measuring model impact. Develop models for operational data using Python, SQL, dbt, Snowflake, and AWS; define evaluation and drift monitoring; conduct A/B tests or causal analyses; and partner with product and engineering teams to embed ML into customer workflows.

Designs, develops, tests, and supports reliable data engineering solutions for insurance data products. Responsibilities include data transfers, profiling, validation, quality management, issue resolution, data modeling, workflow management, code reviews, troubleshooting, and automation. The role collaborates with technical teams and business stakeholders to translate requirements into scalable, maintainable solutions while exploring AI-assisted engineering practices.

Own and develop Azure Databricks data-platform workstreams across data modelling, medallion architecture, Delta tables, data quality, infrastructure, CI/CD, DevOps, security, and governance. The role requires autonomous delivery from scoping through production, troubleshooting Azure infrastructure, managing Terraform and Azure DevOps, protecting PII, and engaging technical and business stakeholders globally.

Own and modernize end-to-end financial data pipelines, predictive models, and cloud infrastructure. Build real-time market-data ingestion, migrate services to AWS, rewrite PHP calculations in Python, and manage time-series and search databases. Implement data quality, monitoring, alerting, containerized deployments, CI/CD, and GitOps workflows. Collaborate with quantitative researchers and analysts while mentoring engineers and guiding architecture.

Own trading data end to end, including vendor evaluation, ingestion, storage, schemas, production pipelines, quality monitoring, and delivery to traders and models. Lead data product reliability, performance, point-in-time correctness, and backtesting support while managing vendors and growing an engineering team.

Analyze marketing effectiveness through incrementality testing, marketing mix modelling, causal analysis, and business performance investigations. Translate analytical findings into recommendations for marketing investment, campaign optimization, and performance expectations. Build repeatable decision frameworks, collaborate with marketing and technical stakeholders, validate results, and communicate assumptions and uncertainty clearly. The role requires Python or R, SQL, statistical modelling, experimentation, and strong commercial judgment, with opportunities to develop toward senior-level ownership.

Own and modernize Xeneta’s end-to-end data architecture, including Snowflake and dbt models, data pipelines, governance, metadata, lineage, and quality automation. Improve data discoverability and reliability for engineers, analysts, data scientists, and AI agents. Establish scalable standards, automate access and onboarding processes, and educate teams on AI-enabled data workflows.

Build and maintain Databricks data pipelines, data models, automated Python workflows, dashboards, reporting layers, and REST API integrations. The role focuses on data quality, governance, and translating business needs into decision-ready solutions for investment and asset management teams. It also includes developing internal tools with Streamlit, FastAPI, or HTMX and collaborating with technical and non-technical stakeholders.