Top Data Jobs in London

Design, build, deploy, and optimize scalable AI and machine learning systems using large datasets. Develop predictive models, recommendation systems, personalization engines, forecasting solutions, generative AI applications, and production ML pipelines. Collaborate with data, backend, and product teams to integrate models into applications, while monitoring performance, data quality, drift, reliability, and cost. Establish best practices for evaluation, experimentation, reproducibility, and deployment, and research emerging AI technologies.

Translates business and reporting needs into data, MI, KPI, and dashboard requirements. Investigates datasets, reports, data processes, discrepancies, and quality issues using SQL and analytical techniques. Documents business and technical requirements, metrics, data mappings, user stories, acceptance criteria, and recommendations. Collaborates with stakeholders, MI developers, data analysts, engineers, and architects in Agile delivery environments.

Leads the development of self-service data and AI platforms, analytical frameworks, AI-enabled workflows, and scalable data products. Combines on-chain and off-chain data to answer complex product and business questions, build trusted metrics, identify trends and risks, and support decision-making. Requires expert Dune and SQL skills, deep DeFi knowledge, and hands-on experience with Python, ClickHouse, Airflow, data engineering, and AI-enabled analytics.

Provides privacy and data protection consulting to multiple clients, covering privacy audits, gap analyses, DPIAs, policy development, AI privacy assessments, supplier due diligence, breach response, regulatory communications, data subject requests, training, and risk reporting. Advises business, legal, and information security stakeholders across varied sectors while managing client engagements and a small consulting team.

Owns the end-to-end testing strategy, automation framework, and quality assurance for data warehouses, ETL pipelines, data models, AI tools, BI solutions, and data lakehouse projects. Leads test planning, execution, defect management, data validation, performance testing, reporting, and stakeholder assurance. Manages and develops a team of test analysts while partnering with engineers, architects, project managers, and business stakeholders across insurance functions.

Embed with strategic customers to design, build, and implement data-driven AI solutions that improve customer support automation and Fin adoption. Analyze workflows and business challenges, establish measurement best practices, prototype and scale AI tools, and collaborate with Sales, Success, Product, and Engineering. Feed deployment insights back into product strategy while translating complex technical analysis into measurable business outcomes.

Own the full lifecycle of data products for the iLottery division, translating customer and business needs into governed datasets, semantic models, dashboards, APIs, and data feeds. Prioritize backlogs, coordinate cross-functional delivery, establish data quality and governance standards, and oversee adoption, observability, incident response, and continuous improvement. The role requires experience with data platforms, pipelines, analytics, cloud architectures, BI tools, agile product management, and data governance.

Owns delivery of assigned data projects while leading PMO governance for a portfolio of approximately 20 integration and data transformation initiatives. Responsibilities include portfolio reporting, resource planning, risk and dependency management, prioritization, steering committee facilitation, benefits tracking, stakeholder communication, and coaching project teams. The role establishes delivery standards, supports Scaled Agile practices, and promotes consistent Azure DevOps usage across the Data Team.

Own production Python data pipelines, ETL/ELT workflows, and the ontology powering an AI platform. Integrate warehouses and business applications with graph databases and vector stores, while managing deployment tooling, cloud infrastructure, monitoring, and security. The role begins as hands-on individual contribution and develops toward technical leadership through mentoring and architecture ownership.

Lead enterprise-wide data architecture strategy, governance, modeling, integration, migration, and technology roadmaps. Establish data principles, reference architecture, data models, quality, metadata, lineage, and the UK Enterprise Data Model. Guide data transformation, cloud platform adoption, Snowflake initiatives, and data mesh implementation. Mentor data architects, engineers, and analysts while influencing executive stakeholders, evaluating emerging technologies, and aligning data capabilities with business, analytics, AI, healthcare, and connected-care objectives.

Lead and mentor a data science team developing machine learning systems for threat detection and agentic AI. Set ML technical strategy, standards, roadmaps, and deployment practices; guide projects from research through production; collaborate with security, engineering, and product teams; develop advanced malware and behavioral threat models; communicate insights; and contribute to research, publications, patents, and hands-on technical work.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

Leads biomedical data science for clinical and health research, integrating clinical, observational, EHR, genetic, wearable, electrophysiological, and video data. Develops signal-processing methods, machine-learning models, probabilistic and time-series analyses, scalable data pipelines, and reproducible workflows. Validates clinically relevant models, supports disease phenotyping and predictive modeling, ensures compliance with pharmaceutical and healthcare standards, and communicates technical findings to clients and stakeholders.

Provides data centre support on the afternoon shift, covering an existing vacancy. The role supports Iron Mountain’s data centre operations within its broader information management and digital infrastructure services.

Build end-to-end data applications, APIs, internal tools, and user interfaces using AI-assisted development. Work across Python backends, React frontends, data models, Databricks, dbt, pipelines, cloud infrastructure, testing, security, and performance. Review AI-generated code, establish guardrails, improve engineering workflows, and help shape application architecture and technical direction.

The Business Consultant will engage with clients in public sectors, driving technology-enabled change via business analysis, strategy definition, and stakeholder management while mentoring junior staff.

Clean, transform, document, and upload customer business data into Watershed templates. Advise customers on data solutions, apply data-cleaning best practices, ensure traceability for verification, and synthesize feedback to improve services and software. The role requires strong Excel skills, analytical judgment, attention to detail, customer empathy, and clear communication.

Own the strategy, roadmap, governance, delivery, and lifecycle of a Premium data product within a Finance Transformation program. Lead discovery across insurance and finance data flows, align definitions and quality standards, manage cross-functional teams and dependencies, and drive adoption of trusted, reusable data for reporting and decision-making.

Lead the analytics team, manage analyst performance and development, prioritize business data requests, and deliver reporting, dashboards, analysis, and operational controls. Partner with Product and cross-functional teams on feature tracking and reviews, maintain data and coding standards, oversee automation and tooling, and ensure data quality. The role requires strong SQL, BI, stakeholder management, data storytelling, and the ability to translate business needs into technical requirements within a regulated fintech environment.

Owns the architecture, development, and scaling of data products, backend data services, ingestion engines, APIs, and infrastructure on AWS and Snowflake. Collaborates with engineering and data teams, automates infrastructure using AWS CDK, optimizes reliability and latency, and ensures data quality through testing and validation. The role applies a product mindset to customer-facing and internal data services within an EV charging and smart energy platform.

Build and improve production AI systems for fintech operations, including chatbots, document extraction pipelines, LLM workflows, and autonomous account actions. Responsibilities span opportunity discovery, rapid prototyping, prompt and retrieval optimization, deployment, evaluation, monitoring, and process automation. The role requires strong Python engineering, practical LLM experience, sound judgment in ambiguous situations, and the ability to communicate trade-offs and influence stakeholders.

Designs and delivers cloud-based data solutions for clients, advises on data landscapes, governance, quality, and maturity, and shapes data strategies and roadmaps. Leads requirements workshops, develops scalable solutions using Azure, Fabric, Databricks, SQL, and Python, implements security practices, manages multiple delivery workstreams, supports junior staff, and contributes to proposals and business development.

Build, scale, and operate Databricks and AWS infrastructure supporting automated, reproducible machine learning workflows. The role includes infrastructure-as-code with Terraform or Pulumi, containerized platforms using Docker and Kubernetes, system architecture, reliability and cost optimization, and cross-functional collaboration with data and ML teams. The engineer will lead complex projects, improve platform scalability and usability, document systems, and mentor peers while operating production-scale data and ML platforms.

Design, build, and operate scalable data pipelines, models, cloud platforms, and AI applications on Google Cloud. Lead technical delivery for client projects, make architecture decisions, manage risks, and ensure production quality through testing, observability, and data controls. Build and evaluate LLM applications, collaborate with consultants and data scientists, communicate with technical and non-technical stakeholders, and raise engineering standards through reviews, documentation, and reusable practices.

Coordinate end-to-end media data delivery for the Cockpit program across multiple markets. Manage refresh calendars, stakeholder communications, vendor coordination, data quality assurance, discrepancy resolution, governance, and process standardization. Ensure accurate and timely media data submissions that support marketing mix modeling and business decisions. Manage deadlines, risks, blockers, and ad hoc refresh requests while communicating technical data issues clearly to technical and non-technical stakeholders.