Top Data Jobs in London

Leads client account growth through senior technical advisory, opportunity development, solution design, and engineering governance. Translates business needs into scalable, secure architectures spanning cloud, data, APIs, AI, DevOps, and full-stack engineering. Builds trusted C-suite relationships, supports proposals and business cases, guides delivery teams, ensures technical quality, develops engineering propositions, and mentors engineers and architects.

Owns the full sales cycle for Data and AI solutions, from customer discovery and data-readiness assessments through proposals, negotiation, and close. Develops pipeline, sells modern data platforms, governance, analytics, cloud, and applied AI services, and builds relationships with strategic technology decision-makers and Microsoft partners. Translates technical capabilities into business outcomes, supports bids and tenders, forecasts accurately, and drives follow-on transformation and AI adoption.

Grow Scale’s UK AI business across public sector and enterprise markets. Responsibilities include researching accounts, building pipelines, developing partnerships, shaping AI opportunities, managing framework applications and public-sector tenders, and creating proposals and executive materials. The role combines business development, commercial strategy, customer engagement, and bid management in a fast-paced market-entry environment.

Develops reliable risk datasets, KRIs, dashboards, management reporting, and early-warning capabilities across enterprise and operational risk areas. Partners with risk stakeholders to define indicators, assess data quality, analyze trends and anomalies, calibrate thresholds, and translate requirements into practical data solutions. Uses SQL, Python, visualization tools, Snowflake, AI-enabled workflows, and automation to modernize risk reporting while maintaining validation, traceability, and human oversight.

Own the requirement-to-reporting lifecycle for compliance analytics, translating business needs into functional specifications, metrics, data mappings, transformation logic, and reporting products. Partner with business leaders, data engineers, and executives to address data gaps, define KPIs, build scalable pipelines, conduct UAT, validate data integrity, and deliver intuitive dashboards using Tableau, Power BI, Mode, or similar tools.

Own and improve Wise’s marketing data activation ecosystem, including Reverse ETL pipelines, SQL/dbt models, monitoring, observability, identity resolution, and advertising-platform integrations. Diagnose end-to-end data flow issues and collaborate with analytics, data, engineering, and paid media teams to deliver reliable customer audiences and conversion signals.

Lead Perk’s 40-plus-person data organization and define its company-wide data strategy, infrastructure roadmap, governance, privacy, and AI adoption. Build data into core product infrastructure, improve quality and scalability, shift the organization from reactive reporting to strategic insight, and develop senior leaders. Partner with Product, Engineering, Finance, Marketing, Revenue, Customer Care, and People leadership while owning the data budget, organizational design, and executive-level outcomes.

Lead strategic and operational data science through quantitative analysis, forecasting, experimentation, and data storytelling. Mentor data scientists, advise cross-functional leaders, define metrics and forecasts, and build scalable decision-support systems. The role also contributes to AI and machine-learning systems that optimize product behavior, pricing, risk, allocation, and customer experiences, with an emphasis on rigorous validation, governance, and measurable outcomes.

Develop, test, and support reliable, high-performance distributed Java applications for real-time streaming and concurrent workloads. Partner with stakeholders, present and review designs, improve reliability/observability/performance, and support production releases to meet SLAs within BGM Data & Analytics.

Own the end-to-end lifecycle of proprietary private markets data across EMEA, including stakeholder outreach, data acquisition, validation, maintenance, quality improvement, and coverage growth. Build relationships with fund managers, investors, and clients; identify valuable data opportunities; apply analytical judgment and AI-enabled workflows; and contribute to strategic and revenue-generating initiatives while ensuring operational excellence and reliable execution.

Leads software engineering strategy and development for AI/ML data platforms. Designs reusable frameworks, architects governed agentic AI development workflows, builds secure and scalable cloud-native applications, and operates 24x7 tier-1 services. Influences technical, product, and business leaders; drives adoption of engineering methods, responsible AI controls, automation, and platform capabilities across teams.

Designs and operates production-grade data pipelines and governed data products for technology resiliency and recovery-risk analytics. Responsibilities include data modeling, integration, lineage, quality controls, metadata management, distributed systems, database optimization, and scalable architecture. Partners with engineering, cybersecurity, controls, architects, analysts, and business stakeholders to translate requirements into technical solutions and deliver measurable operational outcomes.

Develop AML risk detection systems using supervised, unsupervised, semi-supervised, and generative AI techniques. Build and evaluate machine learning controls, optimize precision-recall trade-offs, create investigator insights, automate data labeling and retraining pipelines, deploy production-grade Python services, and collaborate cross-functionally with operational teams. The role also includes mentoring junior colleagues and solving financial crime problems at global scale.

Establish SAP’s Total Rewards analytics and reporting capability for more than 100,000 employees globally. The role will design scalable data architectures, compensation reporting, data models, BI dashboards, HTML reports, self-service tools, and AI-powered workflows. It will automate compensation-cycle reporting, improve data governance and reproducibility, define quality standards, and serve as the technical and functional lead while partnering with senior HR and Finance stakeholders.

Manage and develop the SuDS through Streetworks Market data and digital platforms, including a pan-London SuDS Asset Register. Lead procurements, coordinate consultants, ensure data quality, respond to stakeholder queries, establish data collection APIs, manage data sharing, conduct monitoring and evaluation, and produce GIS mapping visualisations and analysis. The role requires advanced GIS, data engineering, web mapping, platform management, and stakeholder engagement skills.

A 12-month placement supporting Hilton’s Global Safety & Security team through data analysis, risk assessment, incident reporting, forecasting, and project management. The participant will analyze accident, claim, insurance, guest assistance, and vendor incident data to identify trends and inform mitigation strategies across occupational, food, fire, physical security, crisis management, and compliance areas. The role includes mentorship, cross-functional collaboration, and structured professional development at Hilton’s EMEA headquarters in Watford.

Develop and support credit risk and pricing models for lending products. Responsibilities include data exploration, feature engineering, statistical analysis, model monitoring, deployment support, documentation, and collaboration with Credit Strategy, Product, Engineering, and Data Engineering teams. The role is designed for an early-career data scientist with strong quantitative skills and an interest in credit and financial services.

Leads enterprise-wide clinical data analytics and visualization for global clinical development programs. Oversees interactive dashboards, patient profile viewers, safety surveillance tools, and study monitoring solutions across oncology and neuroscience trials. Manages analytics and visualization teams, establishes technical standards and governance, integrates clinical data sources, and partners with clinical, medical, biostatistics, and data management stakeholders. Ensures validated, compliant, and efficient analytics delivery in GxP environments.

Supports the design, development, testing, and implementation of AI, machine learning, generative AI, and agentic AI solutions for clinical trial execution and R&D workflows. Develops AI applications for clinical documentation, evaluates model output quality, and ensures regulatory and privacy compliance. Requires programming, data science, machine learning, LLM, prompt engineering, RAG, cloud platform, and AI-enabled automation knowledge.

Supports the design, development, testing, and implementation of AI, machine learning, generative AI, and agentic AI solutions for clinical trial execution and research workflows. Develops AI applications for clinical documentation, evaluates model quality and outputs, and helps ensure regulatory and privacy compliance. Requires programming, data science, machine learning, generative AI, cloud platform, and AI-enabled workflow automation knowledge.

Leads statistical programming activities within a biopharmaceutical environment, supporting clinical research and development programs. The provided description contains no detailed responsibilities or qualification requirements.

Coordinate AI and data governance requests across HR and Employee Experience. Manage intake, triage, routing, documentation, approvals, SLA tracking, stakeholder follow-up, remediation, and escalation for governance cases. Partner with HR, privacy, legal, compliance, risk, control, and technology teams to progress complex or high-risk use cases through approval. Improve ticket quality, workflows, templates, and guidance while maintaining accurate, auditable records.

Build and productionize classical machine-learning models for lead quality and traffic performance, including classification, ranking, anomaly detection, and forecasting. Apply decision science through contextual bandits, experimentation, causal methods, and off-policy evaluation to improve agent decisions. Monitor models, measure commercial impact, establish guardrails, and collaborate with AI engineers, product managers, platform teams, and business operators.

Define and govern enterprise data architecture centered on Snowflake. Design scalable, secure, cost-effective data platforms, pipelines, streaming integrations, data models, and governance practices supporting analytics, data science, and AI. Establish architecture standards, improve data quality and accessibility, guide engineering teams, and lead modernization across cloud-native and legacy systems. Partner with product and engineering teams to manage security, resilience, observability, and regulatory requirements.

Design and deliver secure, scalable AI, GenAI, agentic AI, data, and cloud architectures for public-sector and regulated clients. Lead discovery, solution design, prototyping, and production delivery while advising on security, scalability, monitoring, responsible AI, resilience, and cost. Provide technical leadership to engineers and communicate complex solutions to senior stakeholders across ambiguous client environments.