Top Data Jobs in London

The Lead Value Engineer is a trusted advisor to customers, driving their value journey through analytics, process improvement, and stakeholder engagement, particularly in Finance and Supply Chain.

Manage Calero technology expense and market data inventory records, reconcile invoices, validate cost allocations, investigate variances, and support accounts payable. Administer market data licenses, exchange entitlements, vendor permission files, and joiner/leaver updates. Prepare reporting, support accruals and prepayments, audit processes, and coordinate with suppliers and internal stakeholders in a regulated environment.

Lead DTCC’s data science and AI strategy, research emerging technologies, and develop enterprise-scale prototypes involving machine learning, generative AI, LLMs, and agent-based systems. Manage and mentor data scientists, oversee project delivery and MLOps, establish data governance and modeling standards, collaborate with product and engineering leaders, and measure business impact through KPIs.

Serve as the technical authority for signal processing, inference, and machine learning systems that convert optical signals into clinical brain-monitoring measurements. Lead algorithms from research through validated production implementation, establish standards for uncertainty, calibration, reproducibility, and evaluation, and guide technical reviews, publications, patents, and regulatory documentation. Mentor data scientists and collaborate with R&D, Clinical, and Software Engineering teams.

The job description provides company background but does not specify responsibilities, qualifications, technologies, compensation, location, travel, or clearance requirements for the Digital Product Data Analyst role.

Build production pipelines that transform satellite laser ranging data into geodetic and orbit products. Develop statistical methods for bias detection, outlier rejection, uncertainty estimation, and quality control; analyze station and network performance; and collaborate with geodesy and orbit determination teams. Produce clear reports, plots, and customer-facing metrics from large scientific datasets.

Build and operate backend data infrastructure for satellite laser ranging measurements, including ingestion pipelines, high-volume time-series storage, customer APIs, and secure data delivery. The role requires production backend engineering, cloud infrastructure, containers, observability, testing, and collaboration with front-end, geodesy, and orbit determination teams.

Own production forecasting and strategy models end to end, including data infrastructure, statistical and machine learning model selection, experimentation, evaluation, and decision-ready reporting. The role requires Python and SQL expertise, strong experiment design, and the ability to communicate complex analyses to non-technical stakeholders while collaborating with the analytics team and responsibly using AI.

Analyze customer behavior and travel product performance using large datasets. Translate complex findings into actionable recommendations, dashboards, reports, and presentations for technical and non-technical stakeholders. Collaborate with Product, Design, Data Science, and external partners; help define success metrics and support evidence-based decision-making across travel and disruption initiatives.

Develop and maintain low-latency football data acquisition and analysis systems using social media and other unstructured sources. Build relational database schemas and RESTful API integrations for modeling and trading systems, optimize system performance, and integrate LLM-based pipelines that convert unstructured data into structured outputs. Collaborate with football analysts to ensure data quality and utility.

Develops end-to-end data solutions, including exploratory analysis, experimentation, predictive and prescriptive modeling, machine learning, and generative AI applications. Partners with business, product, and technical teams to solve complex insurance challenges, communicate insights, and support data-driven decisions. The role also involves validating AI-assisted work, applying statistical methods, and following engineering practices such as version control, testing, code review, and reproducible development.

Drive revenue and profit growth for the Discovery and Data Insights practice by generating eDiscovery and related-services opportunities. Build pipeline through account targeting, outreach, campaigns, events, thought leadership, and relationship development. Support sales of managed services and transactional matters, including scoping, pricing, commercial strategy, and practitioner coordination. Partner with marketing and leadership on collateral, messaging, events, and AI-enabled offerings while using CRM and campaign data to track pipeline and conversion.

Leads strategically important data science initiatives across complex business domains. Shapes data strategy, develops measurement frameworks, applies experimentation, causal inference, forecasting, optimization, and decision science, and partners with senior leaders to drive measurable outcomes. Remains hands-on with complex analyses, experiments, models, and prototypes while establishing technical standards, governance, reproducibility, and documentation. Coaches data scientists and analytics engineers and stewards critical data products end to end.

Drive revenue growth and strategic relationships with data centre operators and managed service providers. Develop account plans, identify connectivity opportunities, negotiate contracts, close deals, manage resale and technology partnerships, and lead co-selling activities. Own customer revenue streams, minimize churn, analyze markets, collaborate cross-functionally, and report on sales performance, forecasts, and KPIs.

Responsible for electrical safety across HV, MV, LV, and ELV data-centre environments. The role develops and manages energisation strategies and electrical safety plans, audits switching operations and safety documentation, verifies isolation and earthing, issues high-voltage permits, and ensures compliance with regulations and site procedures. It also involves client coordination, operational issue resolution, mentoring, outage-programme support, incident follow-up, and extensive international travel to Finland on 11/3 rotations.

Leads end-to-end Data, Analytics, and AI projects across business domains, from scoping and planning through delivery, adoption, and operational handover. Coordinates business, data, technology, security, compliance, and external partner teams while managing scope, budgets, timelines, risks, governance, requirements, and stakeholder communications. Ensures solutions meet business, data quality, privacy, security, and compliance expectations and contributes to improved project delivery standards.

Build and maintain Python data ingestion pipelines for Reach’s AWS data lake, integrating APIs, S3, and SFTP sources. Design and operate AWS infrastructure, implement Infrastructure as Code and observability, and work with Athena, Glue, Lambda, ECS, and Redshift. Collaborate with analysts, product managers, and business teams, while documenting solutions, enforcing unit testing, and improving data quality and reliability.

Presents Salesforce data, integration, architecture, governance, analytics, and related solutions to public-sector and nonprofit customers. Conducts discovery sessions, leads workshops, develops customized demonstrations, explains business value, resolves technical objections, and collaborates with sales teams on differentiated solutions. Manages multiple customer engagements, builds trusted-advisor relationships, and travels occasionally.

Supports FP&A transformation by building finance reporting frameworks, extracting and transforming ERP data, maintaining Alteryx automation workflows, and developing planning, budgeting, forecasting, and cash flow models. Partners with finance, IT, business intelligence, and wider business teams to improve data quality and create standardized commercial tools that support data-driven decisions.

Leads client-facing data solution architecture for banking, capital markets, energy, utilities, and travel clients. Designs enterprise data platforms, lakehouses, pipelines, integrations, models, migrations, governance, and AI-ready foundations. Provides technical leadership to delivery teams, conducts architecture reviews, manages risks, supports estimates, and shapes consulting proposals, proofs of concept, and client workshops. Contributes reusable architectures and practice capabilities while balancing technical quality, commercial considerations, delivery feasibility, and regulatory requirements.

Drive revenue growth for MuleSoft and Salesforce solutions among NGO and enterprise accounts. Build trusted relationships with C-suite stakeholders, uncover digital transformation opportunities, manage account strategies, coordinate internal resources, and expand existing and new customer accounts. The role requires technical platform sales experience, API and integration market awareness, account management skills, and the ability to collaborate across large teams.

Conducts non-interventional real-world data research for international markets, including data-source identification, study design, statistical programming, analysis, interpretation, and dissemination. Collaborates with medical, HEOR, and other stakeholders on protocols, statistical analysis plans, reports, abstracts, and manuscripts. Applies observational research methods, communicates findings, manages complex projects, and supports innovation using R and AI tools.

Partner with Product, Design, Engineering and Analytics Engineering to analyze customer behaviour, define KPIs, run funnel/cohort analyses, design and evaluate A/B tests, build dashboards, and surface actionable product insights to drive activation, engagement and retention.

Build and own high-performance distributed data systems supporting multimodal AI workloads. Responsibilities include designing, deploying, monitoring, and scaling storage, indexing, querying, processing, protocols, and APIs. The role requires production service ownership, sound architectural judgment, performance optimization, reliability engineering, and mentoring through code reviews, pairing, and knowledge sharing.

Leads end-to-end data analytics projects supporting strategic commercial, operational, and financial decisions. Builds and governs Domo dashboards and reports, establishes data quality and governance standards, documents data lineage, and presents executive-ready insights. Partners with senior stakeholders across Product Operations, Commercial, Finance, and Leadership to prioritize analytics work, manage competing requests, and communicate trade-offs. Requires strong SQL, BI, data warehousing, ETL, stakeholder communication, and project ownership skills.