Top Data Jobs in London

Design and build end-to-end ML solutions for 1:1 personalization, own data transformation, model training and deployment, provide technical customer guidance, extend product capabilities, and shape BrazeAI strategy.

Build and maintain ultra-high-scale data platforms and pipelines (petabyte scale). Write fault-tolerant Java/Spark code, design low-latency query capabilities, improve performance and stability, participate in technical reviews, and enable customers/internal teams to query and extract analytics from event data.

The Engineering Manager will lead a Data Replay & Recovery Team to build a data replay application, improve service efficiency, and design a data platform for processing large datasets.

Develops CRM segmentation, audience strategies, predictive models, and personalized marketing campaigns using Salesforce platforms and SQL. Maintains audience libraries, validates data pipelines, governs customer data, supports experimentation and performance measurement, and creates lookalike and suppression audiences. Collaborates with Data Engineering, Data Science, IT, and marketing teams to ensure data quality, privacy compliance, campaign accuracy, and operational efficiency. Mentors analysts and marketers while improving CRM processes and personalization capabilities.

Leads CLS’s Cyber Hygiene and Data pillar, establishing enterprise strategies and teams for data protection, application security, vulnerability management, cryptography, data governance, and secure software development. Oversees regulatory and audit readiness, risk remediation, vendors, governance reporting, budgets, and talent development while partnering directly with the CISO in a highly regulated financial-services environment.

Sell Microsoft business applications, Azure, open-source and AWS software and professional services into Public Sector accounts. Lead complex, C-suite sales campaigns (£500k-£2M+), develop accounts and pipeline, forecast accurately, write propositions/bids for structured procurements, manage virtual teams, and deliver quarterly and annual sales targets while positioning AI-enabled and data-driven solutions to translate capability into measurable business value.

Own and maintain governed cost, schedule, benchmark, and project-performance datasets for global data center products. Build data models, validation routines, reconciliation processes, metadata structures, variance analyses, and Power BI dashboards. Partner with product, engineering, procurement, construction, developers, and IT teams to improve data quality, automate reporting, support techno-economic analysis, and provide trusted insights for product and infrastructure decisions.

The Staff Data Scientist will provide technical leadership in Claims, developing ML and AI systems for risk decisioning and fraud detection, collaborating with cross-functional teams to scale decisioning and enhance claims automation.

Lead technical strategy for claims fraud data science, designing and shipping production ML and Generative AI/LLM systems. Drive system-level improvements for model integration, monitoring, feedback loops and QA. Influence cross-functional product and engineering decisions to scale AI-driven claims automation and improve measurement of performance and uncertainty.

Supports data and digital capability design, delivery, governance, and adoption across insurance operations. Translates stakeholder needs into requirements, user stories, process models, solution options, and prioritized backlogs. Partners with platform and delivery teams to assess feasibility, manage dependencies, oversee implementation, and ensure alignment with architecture, controls, and business objectives. Monitors product adoption and effectiveness while improving data literacy and business readiness.

Develop and evaluate machine learning models for recommendations, personalization, customer modeling, and predictive analytics. Engineer features, analyze experiments, communicate results, and collaborate with Product, Engineering, MLOps, Commercial, and Marketing. Contribute to production ML workflows, including deployment, monitoring, testing, reproducibility, and model improvement. Use Python, SQL, AWS, Git, and analytics tooling while applying responsible AI and data-quality practices.

Lead migration strategy and governance for large-scale SAP ECC and S/4HANA transformation programs. Responsibilities include establishing Migration Factory governance, facilitating stakeholder workshops, defining data cleansing and readiness strategies, shaping current-to-target transition approaches, and advising on migration risks, dependencies, and best practices. The role is strategic rather than hands-on, requiring extensive SAP data migration experience and strong stakeholder management.

Leads the design, delivery, and optimization of scalable batch and real-time data platforms on GCP. Responsibilities include building pipelines, data modeling, orchestration, security, governance, monitoring, incident resolution, cost optimization, architectural decision-making, and system integrations. The role provides technical leadership, mentors engineers, translates complex requirements into data solutions, evaluates AI tooling, and owns the reliability, performance, and quality of business-critical data systems.

Leads Haleon’s Data and AI agenda across Europe and MEA, serving as the senior business-facing data leader and primary interface with the Data Office. Owns regional data demand, prioritization, governance, delivery, benefits tracking, and value realization. Leads and develops the OU Data team, partners with senior stakeholders, coordinates global data functions, and drives adoption of enterprise data, analytics, intelligence, and AI standards and capabilities.

Leads enterprise data governance strategy, risk and control frameworks, prioritization, roadmaps, maturity models, and regulatory reporting. Acts as the liaison between Finance Data and International Business stakeholders, identifying requirements, resolving data quality issues, supporting remediation, and ensuring compliance with Citi Data Governance Policy. Requires financial products knowledge, SQL querying, Excel and Python analysis, regulatory reporting expertise, and strong senior-level communication and program management skills.

Leads and develops a team of Data Scientists and other Data professionals, setting strategic direction and identifying high-impact opportunities. Partners with Product, Engineering, and senior stakeholders to shape product strategy and influence decisions through experimentation, causal inference, statistical modelling, machine learning, and analytics. Ensures analytical work drives measurable customer and commercial outcomes while collaborating with other data disciplines on tooling and infrastructure. Also contributes to hiring, coaching, technical standards, and the broader Data Science practice.

Lead a financial crime data science team focused on detecting, preventing, and mitigating financial crime. Partner with Product, Risk, Engineering, and control owners to shape data strategy, generate strategic insights, improve product decisions, and deliver rigorous analytics and machine learning solutions. Hire, develop, and retain data scientists and analysts while supporting innovation through business intelligence, experimentation, causal inference, and self-serve analytics.

Designs, builds, and maintains scalable data pipelines supporting data science and advanced analytics. Manages reliable Airflow orchestration, delivers data engineering solutions using Databricks, and contributes to GitHub-based collaborative development, code reviews, and engineering standards. Partners with data scientists, supports controlled feature-led delivery, investigates root causes, and develops practical solutions. The role also values adaptability, continuous learning, and exposure to MLOps concepts and tooling.

Design and operate production data pipelines and standardized data models for a multi-tenant Azure SaaS platform serving container terminals. Build near-real-time and batch ELT pipelines from operational systems, develop Go-based connectors, implement data quality monitoring, and support source-to-curated transformations across PostgreSQL layers. Collaborate directly with terminal operations staff while owning architectural decisions and platform scalability.

Designs, builds, and maintains scalable data pipelines supporting data science and advanced analytics. Manages Airflow orchestration and delivers data engineering solutions using Databricks. Collaborates through GitHub, code reviews, and shared engineering standards in feature-led environments. Partners with data scientists, owns delivery outcomes, performs root-cause analysis, and continuously improves pipeline reliability, observability, and maintainability. Exposure to MLOps concepts and tooling is desirable.

Designs and evolves scalable data and application architecture for an AI Lab. Defines patterns for data models, APIs, integrations, pipelines, storage, governance, deployment, and observability. Builds reusable services and shared data capabilities while partnering with engineering, infrastructure, security, and data teams. The role requires balancing rapid experimentation with scalability, security, privacy, maintainability, and responsible AI delivery across proprietary products.

Designs and evolves scalable data pipeline, orchestration, and inference infrastructure for autonomous driving data platforms. Responsibilities include developing DAG-based workflow systems, tooling, shared libraries, batch inference capabilities, and high-throughput processing workflows. The role focuses on reliability, performance optimization, resource prioritization, incident response, and collaboration with partner teams to support large-scale data ingestion, processing, evaluation, and machine learning operations.

Translate business and regulatory requirements into reference data models, datasets, data quality rules, workflows, and documentation. Partner with architects and stakeholders across Trading, Compliance, Operations, and Research. Serve as a subject matter expert on equity and ETF reference data, corporate actions, identifiers, lifecycle processing, and downstream impacts. Support data interrogation using SQL and contribute to system delivery, data dictionaries, entity relationship diagrams, requirements specifications, and exception-management metrics.

Manage strategic data centre development programmes across the UK and Europe, leading planning, zoning, permitting, due diligence, budgets, infrastructure coordination, stakeholder engagement, and development agreements. The role supports projects through pre-construction and decision gates, coordinates multidisciplinary consultants and authorities, tracks portfolio risks and timelines, and presents progress reports to clients and leadership.

Conduct primary and secondary research on legal markets, AI, and legal technology. Collect, clean, reconcile, validate, and analyze quantitative and qualitative data from multiple sources. Produce datasets, charts, presentations, and concise written insights while maintaining documentation, data definitions, source records, and quality checks. Apply AI and automation to improve research efficiency while protecting data quality and confidentiality.