Top Data Jobs in London

Supports strategic planning, business performance reporting, budgeting, forecasting, governance, risk management, operational improvement and cross-functional change initiatives for Data & Related Services. The role produces executive dashboards and briefing materials, coordinates business reviews, monitors financial and operational performance, supports resource planning and business continuity, and partners with senior stakeholders across Finance, Risk, Technology, HR and Group Functions.

Lead technical direction for complex data science and machine learning projects, design production-grade solutions, define roadmaps, manage technical risk, support sales engagements, advise customers, establish best practices, and mentor data scientists and cross-functional teams.

Lead and grow a Data Science team to deliver end-to-end, scalable ML/AI solutions for online personalisation. Partner with product, engineering and commercial teams to embed models, set standards for agile delivery, testing and model monitoring, and drive innovation to improve customer experiences and business value.

Design, build and maintain scalable, automated cloud data solutions and pipelines. Provide technical leadership to a small engineering squad, own complex backlog tasks, develop data frameworks and standards, and ensure code quality via reviews and pair programming using Databricks, Python, SQL and Spark.

Design and maintain reliable data solutions, lead a squad of engineers, develop scalable data pipelines, and ensure adherence to data standards.

Lead and develop a Data Science team to deliver scalable ML/AI solutions for an online Personalisation programme, ensuring impactful customer experiences.

Organize and maintain Somerce’s Google Drive, manage permissions, consolidate live, shop, affiliate, and advertising data, run platform exports, and prepare clean datasets for AI workflows. The role requires strong mathematical or analytical education, excellent spreadsheet skills, meticulous organization, and the ability to coordinate with teams to maintain accurate data and file structures. It is office-based in London or Manchester.

Own the integrity and governance of commercial master data across HubSpot, Sage, product systems, Syncari, and reporting platforms. Define data models, source authority, matching, deduplication, hierarchy, mapping, validation, and conflict-resolution rules. Configure and monitor Syncari integrations, investigate failures, reconcile records, improve source-system controls, and maintain the Power BI data model. Document data flows, testing, change control, and exceptions while coordinating with independent system owners.

Lead AI safety projects for frontier labs, developing evaluation frameworks, scientific experiments, and supporting software architectures. Mentor technical teams, shape project scope and feasibility with commercial partners, advance AI evaluation research, and ensure rigorous scientific and technical standards throughout delivery.

Own experimentation and analytics for iwoca’s partner lending channel. Design experiments, apply causal inference, model customer behavior, analyze large datasets, optimize conversion funnels, and translate findings into product and commercial decisions. The role involves shaping analytical practices, evaluating lending product strategies, using AI to accelerate analysis, and collaborating with commercial, strategy, product, engineering, and design teams.

Lead operational planning, resourcing and delivery for the D&I data team. Run team ceremonies, triage inbound requests, maintain tooling, implement process standards, track operational metrics, and directly manage Analytics Engineers and Analytics Developers while coordinating cross-functional stakeholders.

Lead Salesforce Data 360 sales across EMEA, defining go-to-market strategy, driving revenue growth, expanding strategic partnerships, and influencing cross-functional teams. Build, recruit, coach, and retain a high-performing specialist sales organization while collaborating with executives, customers, marketing, finance, product, and recruiting. Represent the organization at internal and external events, communicate complex data platform solutions, and guide organizational transformation and data-driven decision-making.

Serve as a senior analytics partner to underwriting and related teams, translating commercial questions into analyses, dashboards, and actionable insights. Deliver complex analytics workstreams, govern Power BI development and releases, apply SQL and data modeling standards, and support automation and AI-enabled workflows. Coach analysts, conduct peer reviews, collaborate with Data Engineering, and contribute to Agile planning, stakeholder engagement, and external data propositions.

The Finance Data Manager will analyze complex financial and operational datasets, translate business requirements into scalable data assets, and deliver management reporting and commercial insights. Responsibilities include collaborating on data pipelines, automating financial reporting with code and AI, building reporting solutions, improving processes, and partnering with stakeholders across the business.

Develop and deploy predictive and prescriptive models, AI/ML roadmaps, MLOps practices, anomaly detection, forecasting, and IoT analytics for infrastructure and environmental projects. Collaborate with engineering and cross-functional teams on data pipelines, streaming analytics, and data quality. Conduct experiments, communicate findings to technical and non-technical stakeholders, and mentor junior data scientists and analysts.

Designs, develops, governs, and optimizes cloud data pipelines and platforms using Azure Databricks and Azure Data Factory. Owns data engineering quality, DevOps, CI/CD, release governance, performance optimization, data quality, and service SLAs. The role serves as a technical authority, mentors engineers, manages supplier standards, collaborates with data science and architecture teams, and communicates with business stakeholders. International travel to London-based operations is required at least twice annually.

Build and operate production data pipelines using Python, SQL, Airflow, Snowflake, and Databricks. Implement ingestion sources, DAGs, schemas, monitoring, catalog registration, reverse ETL, and governance controls. Investigate pipeline failures, perform lineage analysis, review code, identify downstream risks, and support data quality, security, and access controls. Collaborate with stakeholders, clarify requirements, document escalations, and mentor junior data engineers.

Owns data science products end to end, including problem framing, data preparation, modeling, deployment, monitoring, retraining, and retirement. Partners with underwriters and actuaries to improve pricing, profitability, automation, and operational efficiency. Designs production-grade ML pipelines, raises MLOps and CI/CD standards, manages stakeholder requirements, evaluates external data and generative AI solutions, and coaches data scientists and analysts in a regulated insurance environment.

Leads enterprise Data and AI product strategy while hands-on designing data architecture, scalable platforms, AI agents, automation workflows, and governance frameworks. Builds and scales LLM- and ML-powered products, feedback loops, and responsible AI controls. Partners with engineering, data science, consulting, business, and technology vendor teams; presents roadmaps and business cases; mentors technical talent; and drives adoption, ROI, and measurable business impact.

Leads a team responsible for the reliability, performance, and availability of GPU-accelerated HPC infrastructure. Oversees monitoring, incident and problem management, root cause analysis, automation, documentation, change management, and operational metrics. Owns Agile delivery practices, backlog alignment, cross-functional coordination, and continuous improvement. Acts as an escalation point during major incidents while managing team development, resource allocation, stakeholder communication, and third-party support relationships.

The role involves using SQL and Python to develop automated solutions for risk management, improve process monitoring, and collaborate with various teams to enhance data-driven decision-making in banking operations.

Lead technically informed sourcing and procurement for electronic components and EMS/CM partners. Own RFQs, cost modeling, supplier negotiations, qualification, NPI sourcing, BOM cost analysis, lifecycle management, risk mitigation, and dual-sourcing strategies. Collaborate with engineering, manufacturing, quality, finance, and program teams to optimize cost, availability, quality, and scalability across the product lifecycle.

Build and operate globally distributed data-transfer infrastructure for video, LiDAR, and sensor data at petabyte-per-day scale. Design parallelized pipelines using Flyte, Kafka, Azure, Python, and Terraform; implement reliability, observability, integrity checks, backfills, dashboards, alerting, and cloud-to-cloud networking. Collaborate with product and external partner teams, support evolving requirements, and participate in an out-of-hours on-call rotation.

Analyzes, cleanses, validates, and enhances customer risk and control data within the CUBE Network. Supports data matching using Python-based tools, collaborates with subject matter experts, and works with Product and Engineering to improve processing workflows. Helps define risk and control indices, automates analysis, and maintains high standards of data quality, consistency, and accuracy.

Design, build, deploy, and optimize scalable data pipelines and ETL/ELT processes using Azure Databricks and related Azure services. Develop data models, data quality frameworks, integration solutions, and technical documentation for insurance data transformation programs. Collaborate with stakeholders, lead technical delivery, and apply data governance, warehousing, distributed computing, Agile, and DevOps practices.