Top Data Jobs in London

Lead Data and AI transformation consulting for commercial insurers and reinsurers. Advise C-level stakeholders, define AI-enabled operating models, lead complex transformation programs, and deliver measurable business outcomes across underwriting, claims, operations, and distribution. Expand Synpulse’s Data and AI practice through proposition development, capability building, mentoring, client development, and proposal leadership while translating emerging GenAI and Agentic AI trends into practical insurance solutions.

Senior Consultant serving as a business-facing reinsurance data analyst in a consulting environment. The role analyzes reinsurance data structures, definitions, and flows; translates business needs into data requirements; validates data using SQL and Python; and supports data products, analytical datasets, and transformation initiatives. It requires strong reinsurance knowledge, stakeholder management, consulting skills, and collaboration with engineering and architecture teams.

Leads consulting engagements for insurers and reinsurers, translating business strategy into data- and AI-enabled operating models, capability maps, transformation roadmaps, semantic models, and structured solution designs. The role facilitates workshops, aligns business and technology stakeholders, assesses data landscapes, identifies AI opportunities, supports implementation teams, and develops reusable data and AI proposition assets. It requires strong insurance domain knowledge, business architecture, data modeling, analytical, communication, and consulting capabilities.

Develops and executes sourcing and category-management strategies for data center-related direct and indirect spend. Leads spend analysis, supplier selection, RFPs, contract negotiations, cost optimization, implementation, supplier compliance, and long-range sourcing plans. Partners with internal stakeholders and senior management to deliver savings, governance, and value across construction, engineering, electrical, mechanical, facilities, and infrastructure categories.

Develop data science and marketing effectiveness solutions for international clients. Apply econometrics, statistical machine learning, AI, Marketing Mix Modeling, and customer analytics end to end, from data gathering and analysis through modeling and presentations. Collaborate with clients, contribute to research and development, strengthen data engineering capabilities, and support company culture and sustainability initiatives.

Own the data integration and product roadmap supporting Grip’s AI and agentic systems. Diagnose integration issues, guide API and ETL pipeline work, manage CDP tracking and data quality, define metrics, and shape data for AI consumption. The role will expand into MCP tool design, agentic workflows, AI architecture, monitoring, evaluation, and security while coordinating agile ceremonies and engineering delivery.

Leads the technical direction of data engineering, owning scalable pipelines, warehouse architecture, data models, ELT transformations, quality monitoring, and platform reliability. Defines engineering standards, reviews designs and code, drives CI/CD and observability, resolves complex technical issues, and mentors engineers. Collaborates with analytics, reporting, data science, and platform teams to translate requirements into performant, cost-effective solutions while contributing to warehouse modernization and technical planning.

Lead and grow a team of ML engineers to build and operate production ML and LLM systems—real-time inference APIs, batch data products, and agentic AI—ensuring deployment, monitoring, CI/CD, reproducibility, and measurable business impact.

Develop analytical methodologies for complex retail datasets, applying statistics and machine learning to consumer behavior and business performance. Build and test Python and SQL solutions, develop features for retail media platforms, create AI agents, provide analytical consulting, and communicate findings to technical and non-technical stakeholders.

Own enterprise technical support cases from triage through resolution across Kubernetes, cloud infrastructure, ML platforms, authentication, data connectivity, and model deployment. Diagnose issues, write actionable Jira bug reports, create knowledge-base documentation, coordinate global case handoffs, and communicate clearly with customers. The role includes live troubleshooting sessions and participation in an EMEA weekend on-call rotation.

Leads ASOS’s global privacy and compliance agenda as Data Protection Officer for UK and EU operations. Responsibilities include developing privacy and compliance frameworks, advising senior stakeholders, managing regulatory engagement, data breaches, audits, DPIAs, data subject rights, international transfers, third-party due diligence, marketing compliance, and AI governance. The role also oversees ethics and compliance programs covering anti-bribery, anti-money laundering, sanctions, whistleblowing, conflicts of interest, and regulatory engagement.

Design, develop, deploy, and monitor machine learning and AI models, including predictive models, NLP, computer vision, and generative AI solutions. Analyze and transform large datasets, build scalable data pipelines, automate ML workflows, evaluate model performance, and integrate models into production. Collaborate with technical and business stakeholders to deliver actionable insights and continuously improve AI products.

Two-year graduate programme offering placements across software engineering, data science and AI, data engineering, and cloud computing. Graduates will build software solutions, explore machine learning, work with cloud platforms, and transform complex data into insights while supporting EDF’s energy and net-zero goals. The role includes remote work, office and site placements, possible temporary relocation, expert mentorship, and professional development.

Analyze data, identify trends, create reports and dashboards, support data governance and quality, gather business requirements, improve processes, and contribute to change and transformation initiatives. The role requires strong analytical, problem-solving, communication, and stakeholder engagement skills while working on high-profile client projects and potentially working on-site up to five days per week.

Graduate consultants analyze datasets, build dashboards and visualizations, support data pipelines and transformations, ensure data quality and governance, and communicate insights to stakeholders. The role involves client-facing, on-site work within transformation programs, supported by structured training, mentoring, and professional accreditations. Opportunities span data analysis, business intelligence, data engineering, governance, risk and compliance, and data science.

Leads CRM data strategy, governance, quality, compliance, integrations, reporting, and analytics. Acts as the Salesforce and CRM data subject matter expert, supports AI and predictive analytics initiatives, and delivers customer, marketing, and commercial insights. The role collaborates with business and technical stakeholders, manages external partners, and coaches a CRM Data Executive while driving data capability improvements and informed decision-making.

Lead index and benchmark data management across the product lifecycle: own data quality, governance, cataloging, documentation, and risk. Partner with product, technology, and business teams to drive data-driven product innovation, ensure compliance, and promote data literacy while leveraging AI and cloud platforms.

Lead product strategy and roadmap to re-imagine the advisor/client trading experience. Define requirements, partner with design and technology, manage delivery across scrum teams, pilot and roll out capabilities, and use user research and metrics to drive prioritized features while maintaining stakeholder relationships and control requirements.

Lead design, delivery and maintenance of on-prem and cloud market-data distribution systems (real-time, historical, AI/ML use cases). Collaborate with product/stakeholders, provide 3rd-level operational support, validate AI-assisted recommendations, ensure resiliency, security, auditability and strong process controls.

Lead design and delivery of cloud-native data and AIML production systems. Build data engineering, backend, Cloud infra/DevOps and MLOps stacks, produce secure production code, architect microservices and distributed, data-intensive applications, and drive adoption/governance of AI-assisted engineering practices while communicating with stakeholders.

Lead design and deliver cloud-native, production-scale data and AI/ML engineering solutions. Build data engineering, backend, DevOps and MLOps stacks; produce secure production code and architecture artifacts; troubleshoot complex systems; collaborate with stakeholders and engineering communities to industrialize AI/ML models.

Lead design and delivery of high-performance, real-time data pipelines and analytics for trading. Drive AI-enabled data engineering, mentor engineers, manage global initiatives, and improve SDLC, testing, and operational reliability.

Design, build, and maintain large-scale, secure data engineering solutions. Lead architecture, deliver production code, drive automation and operational stability, mentor teams, and evaluate vendor/technology choices to advance risk technology platforms.

Lead reliability, performance, and automation of analytics pipelines and reporting. Define SLIs/SLOs, monitor data quality/lineage, enable self‑service dashboards, enforce data risk/controls, drive FinOps/capacity insights, lead stakeholder reviews, and mentor junior engineers.

Design, build, and operate cloud-native lakehouse and streaming data platforms and pipelines using Python, PySpark, dbt, Airflow, and orchestration/infrastructure tooling. Ensure performance, reliability, security, and observability; model and transform data for analytics; deploy with IaC, containers, and Kubernetes; mentor engineers and drive platform standards and best practices.