Top Data Jobs in London

Own data quality, governance, reporting, and intelligence across CRM, marketing, and client relationship platforms. Develop pipeline, cross-sell, and client growth insights; establish data stewardship models; lead AI-enabled enrichment and predictive data-quality initiatives; and translate complex analysis into actionable recommendations for senior stakeholders and growth teams.

Build and operate scalable cloud data pipelines, warehouses, transformation layers, and ML production systems. Lead data infrastructure architecture, warehouse performance optimization, DataOps monitoring, CI/CD improvements, data quality testing, and large-scale data product delivery. Collaborate across teams, promote data stewardship, and support batch and streaming pipelines while deploying, serving, and monitoring machine learning models.

Lead analysis of operational data to support decision-making. Build dashboards and reports, partner with operations stakeholders to identify process improvements, monitor KPIs, and inform strategic initiatives.

The Senior Data Scientist will lead the delivery of data science solutions, mentor teams, and develop ML models to address business challenges.

Integrate third-party systems, internal applications, SharePoint, and Microsoft 365 with Databricks. Build reusable data products, internal applications, and AI-enabled workflows for fund management, asset management, and finance. Establish access, security, cost-management, deployment, and documentation standards while consolidating legacy workflows and partnering with central data platform teams.

Owns the global product performance benchmarking capability for data center infrastructure. Defines methodologies, baselines, KPIs, WBS/CBS structures, regional factors, confidence ranges, and refresh governance spanning cost, schedule, sustainability, and operational performance. Partners with Product, Engineering, Delivery, Procurement, Operations, Sustainability, Analytics, consultants, and senior leaders to ensure benchmark outputs support RFPs, business cases, pricing, customer discussions, product decisions, and executive reporting.

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.

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.

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.

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.

Five- to six-month data engineering internship supporting quantitative research and trading platforms. The intern may work on data acquisition, large-scale datasets, data stores, APIs, production datasets, and real-time or batch data infrastructure. Responsibilities emphasize reliability, performance, scalability, and collaboration with researchers, traders, and engineers. Candidates should have computer science fundamentals, Python experience, structured problem-solving skills, and clear communication. SQL or NoSQL knowledge, front-end experience, and financial markets interest are preferred.

Lead CRM analytics for Personal and Business segments: design measurement frameworks, run campaign and lifecycle analyses, apply A/B and incrementality testing, build segmentation models, maintain dashboards, ensure data quality, and partner with CRM, Product, and Data teams to optimise customer engagement and retention.

Manage European tax compliance and reporting across multiple countries, including corporate income tax, VAT, and property taxes. Review external tax filings, conduct research, support audits, communicate with tax authorities, and oversee tax projects. Improve tax processes through automation and technology while managing deadlines, shifting priorities, and cross-functional collaboration.

Own Civica’s enterprise data and integration architecture strategy, defining current and future-state architectures, scalable data models, integration patterns, governance standards, and migration strategies. Lead architectural direction across ERP, professional services automation, data transformation, and integration programs. Advise executives and partners, support data security and compliance, and establish enterprise-wide master data management and single-source-of-truth foundations for analytics, automation, and AI.

Leads NTT DATA’s London Client Experience Centre briefing program, managing executive engagement strategy, agenda design, event execution, content quality, budgets, vendors, scheduling systems, and post-engagement reporting. The role advises account and sales teams, delivers C-suite experiences, coaches briefing staff, coordinates events and hospitality, tracks KPIs, and drives continuous improvement. It also supports executive communications, public relations, media relations, analyst relations, and senior stakeholder alignment.

Deliver end-to-end data solutions using Microsoft Fabric, including lakehouses, pipelines, semantic models, and Power BI. Support Data Mesh, Data Product, governance, metadata, lineage, data quality, DataOps, deployment automation, monitoring, and operating model activities. Collaborate with business and technical stakeholders to develop scalable data products aligned with financial services regulations, GDPR, security, and auditability requirements.

Lead administration, maintenance, upgrades, security, configuration, integrations, reporting, and optimization of the Workday HCM and Financials platform. Provide user support, training, documentation, issue ownership, on-call assistance, and guidance on best-practice configuration. Coordinate cross-functional reviews, support feature adoption, identify system deficiencies, and help design new Workday solutions while mentoring team members.

Design, build, and maintain scalable data pipelines and platform services to support analytics, ML, and product teams. Implement infrastructure-as-code (Terraform) and Kubernetes-based deployments, develop data services/APIs, and ensure data quality, observability, security, and compliance. Partner with cross-functional teams, document systems, and continuously improve performance and reliability.

Lead and contribute to building scalable, production-grade data systems for private markets. Architect end-to-end data workflows supporting analytics and AI/ML, ensure observability, testing, and operational maturity, influence technical strategy, engage senior stakeholders, and manage and mentor software engineers while partnering with product, data science, and business teams.

Design and evolve data architecture, platforms, models, integration patterns, and lifecycle practices supporting mission-critical Formula 1 applications. Collaborate with engineers, product owners, and stakeholders on architecture standards, modernization, technical debt reduction, tooling, CI/CD, and roadmap initiatives. Provide technical guidance, participate in engineering ceremonies, and support selected race weekends onsite at Biggin Hill and trackside.

Design, build, and maintain petabyte-scale batch and streaming data pipelines for robotics foundation model training and evaluation. Own data models, storage systems, ingestion, transformation, and orchestration infrastructure. Standardize processing across teleoperation and synthetic simulation datasets while operating production-grade distributed systems and infrastructure.

Build Paddle’s data science function and production decisioning systems from the ground up. Prioritize high-value use cases, personally deliver the first ML or agentic system, and own deployment, monitoring, experimentation, reliability, governance, and financial impact. The role will establish the production ML stack, collaborate across Product, Engineering, Risk, Finance, and Compliance, then hire and lead data scientists and ML engineers. Experience with regulated transactional domains, production ML operations, experimentation, and executive stakeholder management is required.

Deliver technical client deployments of the OneView platform, including data extraction, engineering, data science, cohort development, dashboards, predictive models, and analytics. Manage technical project delivery, stakeholder communication, benefits tracking, client training, testing, and platform improvements. Work with consulting, governance, product, and QA teams to translate client data into actionable insights and measurable social impact.

Build and operate production data pipelines using Python, SQL, Airflow, Snowflake, and Databricks. Implement ingestion sources, DAGs, schemas, monitoring, and catalog registration; investigate and resolve pipeline issues; review code; trace data lineage; support data security, governance, and reverse ETL integrations; collaborate with stakeholders; and mentor junior data engineers.