Top Data Jobs in London

Lead predictive modeling projects from conception through production and monitoring, develop reusable data science pipelines, evaluate features, and guide a team of data scientists. The role partners with senior leaders to shape data science strategy, improve modeling practices, and deliver commercially valuable solutions for insurance pricing.

Lead the end-to-end architecture of UW’s enterprise data platform on Google Cloud. Design a modern lakehouse, dimensional and enterprise data models, streaming and CDC pipelines, governance controls, and AI-ready semantic and retrieval layers. Provide hands-on technical leadership through SQL and Python prototyping, architecture decisions, code reviews, engineering standards, and collaboration with platform engineering, data science, governance, and source-system teams.

Analyze internal and external education data for Camden’s Education Commissioning and Inclusion division. Responsibilities include statutory SEN2 returns, calculating education performance indicators, developing dashboards and self-service data products, delivering bespoke analysis for strategic decisions, and collaborating with data engineering and architecture colleagues to develop data infrastructure.

Leads technical presales engagements for HPE data center networking and WAN routing solutions. Designs scalable architectures, supports complex sales cycles, delivers technical presentations and demonstrations, contributes to proof-of-concept testing and tender responses, and advises customers, partners, sales teams, product management, and engineering. Provides networking thought leadership, conference presentations, mentoring, and technical guidance across routing, automation, network fabrics, SDN, containers, and telemetry technologies.

Lead and develop Data Engineers and Analytics Engineers while setting technical direction for Depop’s data and analytics engineering function. Own scalable data pipelines, analytical models, semantic layers, data quality, testing, governance, observability, and AI-assisted engineering practices. Partner with Data Science, Product, Finance, and Engineering leadership to deliver trusted data foundations for analytics, machine learning, and AI-driven decision-making.

Own the vision, roadmap, metrics, governance, and delivery of IDBS’s scientific Data Platform. Lead data contracts, schema evolution, ingestion, lakehouse architecture, quality practices, regulatory compliance, and customer data integrations. Partner with engineering, informatics, commercial teams, and enterprise customers to deliver governed, reliable data products for analytics, AI, workflows, and external ecosystems. Coordinate dependencies through PI Planning and ensure data integrity, security, resilience, audit readiness, and operational support.

Lead the Data Product and Advanced Analytics function, owning the vision and roadmap for customer intelligence products. Translate complex data opportunities into measurable products, prioritize investments, and collaborate with Data Science, Data Engineering, CRM, Finance, and business teams from discovery through production. Manage and develop three data product professionals while ensuring strong governance, privacy, model evaluation, explainability, and responsible AI practices.

Develop and manage machine learning and statistical models for marketing, commercial, and digital applications. Prepare datasets, evaluate model performance, support deployment, and oversee retraining and monitoring. Apply forecasting, causal inference, Bayesian methods, optimization, experimentation, and multivariate testing to generate business insights. Collaborate with product, engineering, and business teams, contribute to reusable AI foundations, review code, mentor junior data scientists, and promote responsible AI governance.

Develop and improve data science and machine learning models for marketing and consumer insights, including attribution, personalization, forecasting, experimentation, and marketing measurement. Prepare datasets, define evaluation metrics, support model deployment, and generate insights from media, pricing, promotions, and syndicated data. Collaborate with product owners, data engineers, and machine learning engineers; contribute to responsible AI governance, code reviews, reusable AI assets, and mentorship of junior team members.

Leads EMEA electrical operations engineering programs for data center facilities. Oversees acceptance testing, commissioning, maintenance planning, remediation projects, system reliability, risk management, and technical support for site operations. Coordinates subject matter experts, vendors, design and construction teams, and international engineering groups. Reviews equipment submittals, develops failure response procedures, analyzes asset performance and maintenance data, manages corrective actions, and ensures critical infrastructure remains available throughout the facility lifecycle.

Own clinical study data architecture from planning through database lock. Design EDC databases, specifications, edit checks, validation, and analysis-ready data structures; manage queries, data reviews, reconciliations, safety coding, change requests, reports, and audit-ready documentation. Collaborate with Clinical Operations, Biostatistics, Product, and program leadership while using Python, R, SQL, AI, and automation to improve data quality and delivery within GCP and regulatory requirements.

Provides complex investment data management support for enterprise clients and stakeholders. Resolves data integrity issues, performs root cause analysis, oversees specialist activities, and leads process improvements. Supports projects, change initiatives, workflow enhancements, and operational readiness. Serves as a client-facing thought partner, coordinates with internal teams and external data vendors, and applies investment and data management expertise to improve fund oversight and decision-making.

Design, build, and evolve scalable real-time and batch data pipelines and ETL frameworks. Own data platform performance, reliability, quality, and architecture while collaborating with engineering and product stakeholders. Shape ingestion, transformation, storage, and serving strategies; mentor engineers; and support trader-facing data products. The role requires extensive production data engineering experience, distributed processing expertise, cloud infrastructure knowledge, and proficiency with Kotlin, Python, TypeScript, Flink or Spark, Kafka, Redis, and PostgreSQL.

Deliver reports, dashboards, visualizations, and analytical solutions using Power BI, Excel, SQL, and Databricks. Translate insurance data into commercial and operational insights, support data pipeline and reporting migrations, gather stakeholder requirements, automate processes, standardize analytics, document data practices, and advise colleagues on datasets and reporting outputs.

Owns first-line financial crime data, analytics, management information, dashboards, and key risk indicators. Develops automated reporting solutions using SQL, Python, BI tools, and potentially AI/ML; ensures data quality, governance, regulatory compliance, and audit readiness. Partners with risk, compliance, technology, and business teams to identify issues, deliver remediation, manage the analytics roadmap, and present insights to governance forums.

Own the vision, roadmap, backlog, adoption, and value realization for Ipsen’s Data and AI platforms. Coordinate platform engineering, architecture, security, compliance, operations, and business stakeholders. Lead demand intake, prioritization, Agile delivery, service catalog development, platform adoption, governance, risk management, and operational readiness. Co-lead data integration capabilities and ensure scalable, secure, compliant, cost-effective platform services across Databricks, AWS, Power BI, and related ecosystems.

Design, build, migrate, and optimise scalable Azure data solutions, including storage schemas, data pipelines, models, and processing workflows. Use Azure Databricks and Data Factory to integrate and transform large datasets, while ensuring data quality, security, privacy, and performance. Collaborate with product, analysis, development, testing, and analytics teams to deliver reliable data products and support reporting needs.

Support and enhance Azure-based data platforms, pipelines, data warehouses, and Power BI reporting services. Responsibilities include SQL and Databricks development, data ingestion, testing, deployments, CI/CD, monitoring, automation, stakeholder data extracts, and service support through ServiceNow. The role collaborates with Business Analysts and Data Engineers within Agile teams, manages Azure DevOps work items, improves platform reliability, and ensures accurate, secure data delivery.

Develop and productionize recommendation, personalization, ranking, NLP, and GenAI models. Responsibilities include data analysis, feature engineering, model evaluation, error analysis, experiment design, deployment, monitoring, and iteration. The role partners with engineering and platform teams, establishes evaluation frameworks, improves business metrics, documents methodologies, and communicates technical findings to stakeholders while ensuring privacy and regulatory compliance.

Build and scale governed data products, pipelines, analytical models, semantic layers, APIs, and AI-enabled interfaces. Establish standards for data modeling, orchestration, testing, observability, CI/CD, security, and lifecycle management. Partner with stakeholders to define data contracts and product roadmaps, deliver solutions end to end, improve analytics governance, and mentor colleagues. The role requires strong data and analytics engineering expertise, product judgment, and practical experience directing AI coding agents.

Own the roadmap, KPIs, and adoption of APIs, data services, bulk delivery, and AI-native interfaces for patent data. Translate customer and internal team needs into scalable platform capabilities, partnering with engineering and architecture on API design, reliability, performance, versioning, documentation, and modernization. Drive consistent data access across products, evaluate integrations and standards, prioritize production issues, support customer and sales conversations, and improve developer experience.

Perform precise 3D data annotation and labeling in the Cognic platform, follow project guidelines, complete paid training and certification, and maintain quality and productivity standards. The role is fully remote with a flexible schedule and requires reliable computer equipment, stable internet, strong English skills, and at least two years of 3D annotation experience.

Own and grow Eaton’s strategic data centre accounts across the UK by developing account plans, identifying revenue opportunities, managing customer relationships, and overseeing commercial and engineering tender responses. The role requires understanding client requirements, competitors, buying processes, and data centre operations; securing purchase orders and framework agreements; coordinating internal stakeholders; monitoring sales, pipeline, margins, and forecasts; and ensuring effective handover and customer success.

Lead electrical design and engineering for data centre projects across EMEA, from concept through construction support and closeout. Manage design teams, deliverables, schedules, quality, peer reviews, value engineering, and technical decisions. Provide hands-on expertise in power distribution, transformers, UPS systems, and standby generation while mentoring engineers and coordinating multidisciplinary stakeholders. Serve as a customer-facing technical lead, supporting client and contractor meetings, site visits, installation, and commissioning matters.

Develop marketing measurement and effectiveness analyses using client and marketing platform data. Support data processing, statistical analysis, media mix modelling, controlled experiments, KPI forecasting, and budget optimization. Translate analytical findings into actionable client insights while developing expertise in R, Python, SQL, and advanced data science techniques through training and project work.