Top Data Jobs in London

Develop and validate machine-learning and predictive models for Crop Protection R&D using complex scientific, laboratory, environmental, and field-trial data. Collaborate with multidisciplinary scientists and software teams, guide experimental design, support data strategy, communicate insights to technical and non-technical stakeholders, track modelling impact, document methods, and mentor junior data professionals.

Delivers HR people data, reporting, Power BI dashboards, Workday analytics, headcount forecasting, and actionable insights. Supports reward and benefits benchmarking, pay and bonus reviews, pay-gap reporting, incentive design, and benefits analysis. Identifies opportunities to use AI to improve HR processes and contributes to people and culture projects while presenting recommendations to stakeholders across the business.

Develop predictive and statistical models for Crop Protection R&D using experimental and heterogeneous datasets. Collaborate with scientific experts, support experimental design and field trials, and prototype GenAI applications including RAG, knowledge extraction, knowledge graphs, and agentic workflows. Evaluate and govern AI tools, contribute to responsible AI and data governance practices, and work with engineering teams to deploy analytical solutions.

Lead the end-to-end data science strategy for credit, developing and deploying predictive models for underwriting, pricing, fraud, collections, and lifecycle decisions. Build monitoring frameworks for performance, drift, fairness, and governance; analyze portfolio behavior and loss drivers; partner with product, engineering, risk, analytics, and commercial teams; and mentor data professionals while influencing senior leadership decisions.

Lead the design of data collection and reporting processes across international business units. Build executive dashboards and performance frameworks using Excel and Power BI, interpret complex datasets, and provide actionable recommendations to senior leaders. Manage concurrent reporting projects, improve data quality, and support strategic decision-making and business performance.

Lead Zopa’s data privacy function, advising on privacy implications for products and customer journeys, translating UK privacy law into practical guidance, and developing governance frameworks covering DPIAs, privacy by design, retention, ROPA, and third-party diligence. Manage privacy incidents, data-subject rights requests, regulatory notifications, stakeholder partnerships, and a direct report. Contribute to PCI DSS capability, data-risk management, privacy awareness, and compliance across regulated financial services.

Join a hedge fund Compliance Control Room to manage restriction lists, monitor position limits, support MNPI and information barrier controls, field business queries, document and refine controls, gather requirements, and lead data and process improvement initiatives alongside technology teams.

Design, build, and operate DeepL’s Databricks-based lakehouse and ingestion infrastructure (Kafka, dlt). Enable AI-powered data workflows via connectors and integrations, implement data observability and quality frameworks, and steward infra-as-code, CI/CD, access, security, and governance to provide a reliable, scalable data platform.

Design, build and deploy enterprise-scale ML and AI solutions (including LLMs and RAG) to improve operational efficiency, service resilience and customer outcomes. Perform statistical modelling, feature engineering, experimentation, and large-scale data analysis. Move models from prototype to production, support monitoring/governance, and partner with engineering, SRE, product and business stakeholders to define metrics and deliver measurable business value.

Build and operate a petabyte-scale data platform and Capability Factory: ingest, curate, preprocess, and version robot telemetry; design scalable batch/stream pipelines, orchestration, compute and storage; collaborate with ML teams on MLOps and evaluation workflows; build data warehouse and BI dashboards; ensure data quality, access control, and production ownership including on-call incident triage.

Build and maintain device software and data pipelines for handheld gripper data collection devices. Implement sensor integration and temporal synchronization, recording/packaging episodes, validation tooling to detect data faults, operator-facing controls, and on-site support. Debug hardware integration issues and collaborate with hardware, AI, and data teams to ensure high-quality training datasets.

Teleoperate humanoid robots to collect high-quality multimodal datasets, perform precise annotation and labeling, capture video/motion/task logs, follow strict procedures, document dataset quality, identify and escalate anomalies, and contribute workflow improvements. On-site, permanent night-shift role with hands-on interaction with robotic hardware and tools.

Senior AWS consulting leader advising enterprise clients on cloud, data and AI strategy. Lead discovery workshops, shape large-scale transformation programmes, design cloud-native roadmaps, support pre-sales, mentor architects, represent the firm externally, and ensure strategic intent translates into delivery.

As a Data Analyst, you will develop data solutions, conduct diagnostics and analysis for technical projects, and collaborate with clients to design effective outcomes.

Lead CDP strategy and marketing activation: define consumer data model, orchestrate advanced segmentation and personalized journeys, manage end-to-end audience activation, ensure data governance and consent compliance, and enable business users to self-serve on marketing platforms.

Own and build the full financial data pipeline: design Snowflake schema and dbt model architecture, deliver ingestion and transformation with tests and docs, ensure pipeline reliability through financial close (on-call), establish data quality and governance, partner with analytics and finance stakeholders, extend models to OPEX/COGS/ASC 340-40, and support AI consumers with row-level security and freshness SLAs.

Work with Risk Management to gather requirements and develop/enhance risk systems. Build data analysis processes, valuations, and custom measures; create dashboards and reports; integrate systems with trading and middle-office; manage reference data; document processes, implement source control and deployment best practices, and evaluate new technologies.

Evaluate and annotate 3D meshes, GLB files, images, and short videos for model training and error resolution. Identify whether assets are artist-created or AI-generated, and detect geometry defects, topology errors, and scene inconsistencies. Requires hands-on 3D modeling experience, familiarity with Blender or Maya, strong mesh-topology knowledge, English proficiency, and a suitable computer with dedicated graphics capabilities.

Lead end-to-end large-scale financial services transformation programmes, manage governance, stakeholders and budgets, drive SAFe agile delivery (PI planning, backlogs), implement change management, support sales/RFPs, advise senior clients, and foster team development and adoption of digital/AI solutions.

Curate and interpret drug discovery data from scientific papers, patents, and other disclosures. Responsibilities include interpreting chemical structures and biological data, building and maintaining databases, researching biopharmaceutical R&D, and surfacing accurate insights for scientific and industry audiences. The role requires strong data curation, patent literature, chemoinformatics, scientific communication, and attention to detail skills.

Design and deliver enterprise-level data solutions using Azure tools and SQL analytics, focusing on data insights and customer satisfaction.

Lead AI, data science, and data engineering across Europe & RoW fleet business to define AI and data strategy, build scalable data platforms, deliver end-to-end AI initiatives with measurable business impact, drive adoption, and lead a multidisciplinary team while partnering with commercial and operational stakeholders.

Build and operate distributed ingestion and processing pipelines, ensure reliability and performance, define data contracts, add observability and testing, improve platform reliability and CI/CD, and participate in design/code reviews and incident retrospectives.

Lead investigation and resolution of broker margin data discrepancies by validating Python pipeline outputs against internal sources. Debug raw broker files, document margin calculation logic across account types, work with Data Engineering to translate business rules into Python, escalate structural data gaps, liaise with brokers and SMEs, and maintain mapping and audit documentation.

Design, build, and optimise cloud-based data pipelines, ETL/ELT processes and enterprise data platforms for an insurance client. Integrate multiple source systems, ensure data quality and performance, support data modelling and transformation, and collaborate with stakeholders using Agile practices.