Top Data Jobs in London

Performs hands-on data centre support across Sevenoaks and Maidstone sites, including equipment inspections, power cycles, cabling checks, KVM diagnostics, media mounting, hardware control activation, and hot-swappable component replacement. The role requires precision, customer-focused communication, basic server hardware and networking knowledge, security awareness, shift flexibility, driving capability, and eligibility for UK Government and contractual personnel security requirements.

Build and maintain backend-heavy systems for Hudl’s Global Sports Library, including APIs, distributed services, data models, entity-resolution systems, ingestion pipelines, and large-scale data infrastructure. Collaborate with engineers, quality analysts, product managers, and designers; solve complex technical challenges, support frequent deployments, and provide guidance to less experienced engineers.

Lead end-to-end machine learning and AI solutions from discovery through production and optimization. Partner with stakeholders to identify commercially valuable use cases, design scalable predictive analytics applications, and establish best practices across data science, MLOps, experimentation, and software engineering. Collaborate with data engineering, platform, and product teams while providing technical leadership, mentoring, and guidance on production-grade AI products.

Leads the People Analytics data function, managing and developing analysts while delivering dashboards, data products, and complex workforce insights. Translates HR requirements into scalable technical solutions, oversees data quality and governance, defines tooling and technical strategy, and partners with HR, Finance, HRIS, and IT stakeholders. Performs hands-on analysis of complex HR datasets, including attrition, workforce trends, and diversity, equity, and inclusion.

Build and improve client-facing sustainability data solutions, including dashboards, visualizations, interactive reports, and analytics experiences. Translate client needs into product priorities, connect prototypes to client data, validate data quality and calculations, document solutions, and refine products using user feedback. The role combines sustainability and ESG data expertise, analytical judgment, AI-assisted development, client communication, and product experimentation.

Lead end-to-end delivery of production-grade machine learning and AI solutions, from discovery through deployment and optimization. Partner with stakeholders to identify commercially valuable use cases, design scalable predictive applications, establish data science and MLOps best practices, and collaborate with engineering, platform, and product teams. Provide technical leadership, mentor practitioners, support agile delivery, and help advance the organization’s AI capabilities.

Leads the People Analytics data function, managing and developing analysts while overseeing technical delivery, dashboards, data quality, governance, and hands-on analysis. Partners with HR, Finance, HRIS, IT, and senior stakeholders to translate requirements into scalable analytical products and insights. Establishes technical standards, tooling, operating procedures, and data strategy while reviewing outputs and contributing to complex workforce, attrition, and DE&I analyses.

Own and develop metadata-driven ingestion frameworks, orchestration processes, ETL pipelines, and analytical data products using Databricks and Azure services. Optimize scalable, reliable data solutions, implement CI/CD and governance practices, collaborate with stakeholders and data teams, resolve complex issues, and mentor junior engineers.

Own the roadmap, backlog, requirements, prioritization, adoption, and business outcomes for investment-focused data products. Partner with stakeholders across investment, client, operations, risk, and regulatory reporting to define trusted, governed, AI-ready data products. Translate ambiguous needs into requirements, data definitions, acceptance criteria, and quality rules while collaborating with engineering, analytics, and AI teams. Drive adoption, measure value, manage dependencies, and make clear value-based scope decisions.

Lead the design and delivery of governed data products and pipelines on a Snowflake-centric platform. Provide hands-on technical leadership to distributed data engineering teams, establish engineering standards, build AI-ready semantic data products, embed quality and observability controls, and migrate legacy platforms. The role requires strong financial services data knowledge, cloud and modern data architecture expertise, mentorship capabilities, and experience optimizing scalable, production-ready data solutions.

Leads the data engineering function across London and India, owning Snowflake-centric platform architecture, ingestion, transformation, governance, observability, and reusable data products. Builds AI-ready semantic models, modernizes legacy platforms, manages migrations, optimizes costs, and embeds security and compliance controls. Develops a high-performing engineering team and establishes standards for DataOps, CI/CD, data quality, lineage, and scalable data delivery across investment, reporting, regulatory, and AI use cases.

Build and operate production data pipelines for patent, research, and company data using Python and Dagster. Model messy real-world data, develop agent evaluation harnesses, implement data quality monitoring, and deliver reliable data to product stores and APIs. Collaborate with product and ML teams, evaluate new data sources, troubleshoot source failures, and define measurable standards for data quality. The role involves working with large, noisy datasets and shaping how AI agents support data processing.

Design complex, end-to-end customer journeys for high-volume transactional products, from problem framing through outcome evaluation. Collaborate with Product, Engineering, Research, Content, Analytics, and stakeholders; participate in agile sprint planning; use AI tools, research, experimentation, and design systems to improve quality and speed. Mentor designers, contribute to accessibility and design-system adoption, and demonstrate measurable customer and commercial impact.

Provides technical leadership and engineering assurance for HV and EHV utility infrastructure across EMEA. Responsibilities include grid connections, substations, transmission and distribution systems, cable networks, protection schemes, power studies, design reviews, contractor oversight, commissioning, energisation, utility engagement, technical governance, risk management, and evaluation of emerging energy technologies.

Develop pricing data science models using quote, policy, and claims data; conduct claims and price optimization analysis; identify valuable external data sources; deploy real-time pricing models through APIs; and partner with engineering and ML teams to implement pricing strategies and support new product launches.

Define and evolve end-to-end data architecture for real-time news ingestion, enrichment, storage, indexing, and distribution. Develop data standards, schemas, and contracts; normalize metadata for discoverability; ensure observability and lineage; influence platform strategy; collaborate with engineering and product leaders; pragmatically evaluate tools to improve reliability and performance.

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Own the modeling work behind June’s AI interview evaluation engine. Build, evaluate, and deploy models that score and summarize interviews, develop offline and online evaluation infrastructure, and monitor calibration, drift, and bias. Partner with engineering and product to integrate models into the product and improve them through real-world usage. Help establish data-team tooling and standards while working with LLMs, NLP, ambiguous data, and fairness considerations.

Build and operate OrderYOYO’s production data and AI platform, including Microsoft Fabric lakehouse solutions, scalable ingestion pipelines, semantic models, monitoring, data quality, and legacy-platform migrations. Develop with Python, PySpark, SQL, and T-SQL; manage performance, cost, and reliability; and collaborate with Finance, Product, executives, and engineering. Deliver applied AI and machine-learning capabilities such as RAG, agents, churn, recommendations, personalization, fraud, and risk models.

Analyze large-scale security telemetry, threat intelligence, endpoint, and detection data to identify trends, anomalies, and emerging threats. Build dashboards, reports, data pipelines, and KPI frameworks using SQL, Python, LogScale, or similar tools. Collaborate with data engineering, product, and engineering teams to ensure data quality and translate complex findings into actionable insights for technical and non-technical stakeholders.

Analyze and validate traffic datasets, develop methodologies for incomplete data, and create dashboards, visualizations, and reports. Evaluate emerging data collection technologies, identify new insights and revenue opportunities from existing data and footage, and support proof-of-concept projects. The role requires collaboration across international teams, clear documentation, strong stakeholder communication, and adherence to data protection protocols.

Analyze complex insurance data to support business decisions, improve data quality, and resolve issues through validation, profiling, reconciliation, and root-cause analysis. Develop SQL queries, create Power BI reports, communicate insights to varied stakeholders, and support data onboarding, workflow automation, risk management, and process optimization. Collaborate with customers, data providers, and internal teams to improve operational efficiency and data lifecycle processes.

Develop and deliver machine learning and analytics solutions using predictive modeling, computer vision, and generative AI. Improve Verisk products and processes while ensuring accuracy, interpretability, and timely delivery. Stay current with analytic innovations, mentor analytic interns, and share methodologies across the analytics community. Work with large, complex datasets across insurance, energy, financial services, and other industries.

Lead infrastructure engineering for critical market data platforms: provide production support, implement changes, lead architecture and automation workstreams, validate AI-assisted recommendations, ensure performance, resilience and data quality, and collaborate across platforms to modernize and stabilize trading market data infrastructure.

Oversee data risk processes and controls across Latin America and Canada. Assess control design, coordinate risk assessments, maintain control frameworks, manage issues and remediation, monitor standards adherence, and prepare materials for regulatory reviews. The role partners with data, business, Legal, Compliance, Operational Risk, and Audit teams while promoting consistent control practices and influencing senior stakeholders.