Top Data Jobs in London

Build and operate scalable, multi-tenant data infrastructure for millions of Lovable applications. Responsibilities include ingestion, analytical storage, schema management, partitioning, compaction, query performance, isolated agent-written connectors, scheduling, retries, metering, workload isolation, observability, freshness, and recovery. The role requires production ownership across data platform layers while controlling tenant costs and protecting workloads from untrusted or noisy producers.

Own complex analytics projects from business questions through delivery, developing reliable data products and solutions. Partner with product managers, analytics engineers, technologists, and senior stakeholders to shape data-driven strategy. Apply data preparation, blending, governance, and emerging AI techniques while coaching junior colleagues in analysis design, stakeholder engagement, and project management.

Own complex analytics projects from business questions through delivery, creating reliable data products and solutions. Partner with product managers, analytics engineers, technologists, and senior stakeholders to develop data models, semantic layers, and AI-enabled analytics. Apply governance, influence data-driven strategy, adopt emerging tools, and coach junior colleagues in analysis design, stakeholder engagement, and project management.

Lead research, design, and evaluation of large-scale detection models for automated, fraudulent, and malicious activity. Define measurement frameworks for noisy or incomplete ground truth, mentor data science practitioners, advance AI/ML techniques, and shape long-term detection strategy. Partner with ML Engineering, Data Engineering, and Product teams to influence roadmaps and protect Cloudflare customers from evolving threats.

Provides post-sales technical support for enterprise storage customers, resolving and escalating issues, managing technical operations, and supporting installations, upgrades, break-fix work, and expansions. Builds customer relationships, manages bugs and feature requests, supports account teams, and meets SLAs and KPIs. Requires expertise in storage, networking, Linux administration, file systems, scale-out NAS, Bash scripting, and customer-facing technical operations.

Build and maintain production-grade ELT pipelines and a performant BigQuery data warehouse using Dataform. Ingest structured and unstructured data from databases, cloud storage, APIs, and third-party feeds. Develop reporting datasets, data models, validation, monitoring, governance, documentation, and access controls. Collaborate with product, operations, AI, engineering, finance, and BI teams to translate business and financial requirements into reliable, trusted data infrastructure.

Manage and grow major technology and financial services accounts by strengthening executive relationships, developing strategic account plans, identifying cross-sell opportunities, and protecting revenue and gross profit. Monitor contract performance, delivery risks, and opportunities while coordinating sales, marketing, product, and delivery teams to ensure client satisfaction and optimized service delivery.

Support building and maintaining data pipelines, onboard and transform partner data, monitor reliability, validate data quality, automate processes, and document pipeline logic to ensure trustworthy datasets for the business.

Owns HubSpot CRM administration, revenue data governance, pipeline hygiene, forecasting, reporting, and quote-to-cash process improvement. Integrates CRM with billing and finance systems, builds dashboards and pipeline coverage models, supports revenue reporting, and drives commercial team adoption. The role partners with Contracts and Finance to improve data quality, forecast accuracy, reconciliation efficiency, and visibility across long-cycle GPU cloud and colocation deals.

Lead Monzo’s EU Data team and shape expansion strategy across European markets. Own data strategy, measurement foundations, experimentation, forecasting, goal setting, and product-market-fit analysis. Represent Data at executive level, influence commercial and product decisions, build cross-business learning loops, and develop a high-performing full-stack Data organization of individual contributors and managers.

Serves as a strategic talent acquisition advisor to business leaders, developing workforce and hiring strategies, proactive talent pipelines, and high-quality recruiting processes. Leads the full recruitment lifecycle from intake and sourcing through assessment, offers, and onboarding coordination. Uses market intelligence, recruiting metrics, and talent insights to improve hiring outcomes, strengthen employer branding, and enhance candidate experience while ensuring compliance with employment laws and recruiting governance.

Own the vision, strategy, roadmap, and backlog for IG Group’s data products and platforms. Collaborate with data engineering, data science, architecture, infrastructure, product, and business stakeholders to prioritize capabilities, manage dependencies, improve delivery practices, and drive adoption of data tools. Translate technical concepts for varied audiences, define success metrics, engage vendors, and guide iterative development of a scalable data ecosystem using cloud data platforms.

Own and optimize sales operations systems, CRM implementation, pipeline management, account planning, stakeholder mapping, land book maintenance, reporting, presentations, forecasting, and financial analysis. Partner with senior sales leadership and cross-functional teams to improve data integrity, commercial visibility, process efficiency, and coordination across the global sales organization.

The Staff Data Scientist will provide technical leadership in Claims, developing ML and AI systems for risk decisioning and fraud detection, collaborating with cross-functional teams to scale decisioning and enhance claims automation.

Lead technical strategy for claims fraud data science, designing and shipping production ML and Generative AI/LLM systems. Drive system-level improvements for model integration, monitoring, feedback loops and QA. Influence cross-functional product and engineering decisions to scale AI-driven claims automation and improve measurement of performance and uncertainty.

Leads Software Engineering, Data Engineering, and BI teams while overseeing applications, integrations, APIs, EDI, automation services, data warehouses, reporting platforms, and analytics. Drives technology initiatives, engineering standards, release processes, data governance, system reliability, stakeholder alignment, supplier relationships, and continuous improvement across business-critical platforms.

Lead data science for Trainline’s B2B product organization by uncovering growth opportunities, shaping product roadmaps, defining success metrics, supporting experimentation, and driving data-informed decisions. The role requires communicating complex analyses to senior stakeholders, evaluating product engagement, navigating ambiguous datasets, building predictive models, and using visualization and automation tools to deliver measurable business impact.

Develop, validate, and deploy computer vision and machine learning algorithms for clinical decision support using imaging and sensor data. Own projects from messy data and modeling through rigorous evaluation and production integration. Collaborate with hardware, software, medical, and clinical research teams, apply classical and deep learning methods, support regulatory readiness, deliver production-quality Python code, and mentor colleagues.

Own marketing performance analytics across paid, affiliate, influencer, and CRM channels. Develop attribution frameworks, analyze customer journeys and subscription metrics, build Tableau dashboards, and create scalable dbt data models. Use SQL, Python, or R for cohort analysis, forecasting, predictive modeling, and marketing mix modeling. Translate complex findings into actionable recommendations for senior stakeholders and collaborate across marketing, product, medical, finance, and data teams.

Lead end-to-end data science and machine learning across membership, marketing, digital, operations, food and beverage, and events. Build and productionize churn, recommendation, segmentation, and demand forecasting models; establish MLOps, monitoring, governance, and measurement standards; and partner with stakeholders to prioritize high-value work. Coach analysts, review offshore data science work, and explain complex methods and business impact to senior leaders.

Develop and improve machine learning products for pricing, recommendations, and other business areas. Translate ambiguous business problems into practical models, experiments, and evaluations; collaborate with engineering, product, and analytics teams to deploy and monitor production models; investigate performance changes; and communicate insights to support evidence-based commercial decisions.

Build and operate Spotify’s lakehouse and BigQuery platforms at scale. Develop backend services supporting Apache Iceberg, migrate pipelines, manage BigQuery capacity, create monitoring and tooling, and troubleshoot production issues across storage, Kubernetes, IAM, and processing systems. Participate in support and on-call rotations while improving reliability, cost efficiency, and user enablement across global teams.

Build the companys first data stack and data products (internal analytics and customer-facing). Work across data engineering, devops, and software tasks; ship early features, improve developer experience, document proposals, and iterate based on feedback.

Lead analytical data science for Wise’s customer support organization. Measure service quality, diagnose root causes, design metrics, evaluate interventions, and use experiments, causal inference, machine learning, NLP, and LLM techniques to improve customer outcomes. Partner with Product, Design, Operations, and Engineering to guide automation strategy, prioritize opportunities, and influence product and business decisions using customer, conversational, and operational data.

Leads the architecture and delivery of enterprise data platforms and analytics solutions. Designs cloud-native warehouses, lakes, lakehouses, semantic layers, integration patterns, governance frameworks, and modern data stack capabilities. Guides technology selection, roadmaps, technical workstreams, proposals, and client engagements while mentoring engineers and establishing standards for security, quality, DataOps, CI/CD, observability, and Infrastructure as Code.