Top Data Jobs in London

Support product definition, research, and delivery for digital products in financial services and energy. Conduct data and market analysis, map processes, define requirements, support roadmaps and backlog, facilitate stakeholder workshops, and help identify AI-enabled use cases.

The Partner Program Manager will design and manage a tiered partner program, focusing on value-added systems for partner rewards, enablement, and performance analytics.

As an Enterprise Architect, you will lead tech advisory growth, collaborate on solutions, manage client relationships, and mentor junior consultants within the organization, driving technology transformation for clients.

Lead complex engagements in Life Sciences Consulting, managing project teams and relationships, driving strategy and financial outcomes, while fostering a culture of excellence in the team.

The role involves shaping data and AI strategies for financial institutions, guiding clients in harnessing AI for competitive advantage, and leading workshops to align stakeholders.

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.

Design and build end-to-end ML solutions for 1:1 personalization, own data transformation, model training and deployment, provide technical customer guidance, extend product capabilities, and shape BrazeAI strategy.

Design, build, and operate enterprise-grade, cloud-native data platforms and pipelines for Preqin's Company Intelligence. Provide technical architecture, end-to-end delivery, data governance, observability, and reliability while leading and mentoring a small team and partnering with product and analytics to deliver measurable business value.

The Senior Data Scientist will unify technical vision, develop and deploy ML models, and enhance AI strategy across teams. Responsibilities include collaboration, ownership of projects, and maximizing value through AI and data science techniques.

Support investment teams by acquiring, cleaning, and analyzing multiple datasets to test investment theses; identify key data drivers, present findings, and develop decision-support tools while learning fundamental investment research approaches.

The Partner Manager will develop and execute CDW's Cyber Security and Data Protection strategy, driving growth and partner relationships, while collaborating with sales and technical teams to implement effective go-to-market plans.

Lead pricing analytics for Wise Platform: identify strategic pricing and cost problems, perform deep-dive cost investigations, monitor P&L and profitability drivers, improve pricing models and data quality, and liaise with stakeholders to implement pricing and allocation improvements.

Own pricing model and P&L analysis for Wise Platform: investigate cost data, propose pricing and cost improvements, monitor financial performance, improve pricing model and liaise with subject matter experts to surface actionable insights that drive profitability and growth.

Partner with compliance teams to build and own regulatory reporting and compliance monitoring across UK & Europe. Develop advanced SQL reports, maintain data pipelines with engineering tools, design data assets, build visualisations, ensure data quality and documentation, and identify automation opportunities to provide assurance to the Board and regulators.

Generate insights to inform company and customer decisions by curating datasets, surfacing metrics, performing statistical modelling, and deep-dive analysis. Analyze growth drivers, customer segments and cohorts, and link acquisition/activity to marketing and product initiatives to measure realised financial returns. Communicate findings through data visualisation and stakeholder management.

As a Lead Data Scientist for Marketing at Wise, you'll develop predictive models for Customer Lifetime Value, assist marketing strategies, evaluate growth opportunities, and collaborate with Data Analysts.

As a Data Analytics Consultant, you'll lead data transformation for financial institutions, utilizing predictive modeling and advanced analytics to provide insights.

Lead and grow a data platform engineering team to design, build, and operate mondayDB 3.0. Own end-to-end feature delivery, influence architecture and engineering practices, collaborate with Product and Design, and drive scalability, resilience, and observability for massively increased data capacity.

Build and scale a next-generation data platform (mondayDB 3.0). Design architecture, implement backend-heavy full-stack features, collaborate with product and design, ensure CI/CD delivery, and improve performance and engineering practices for large-scale, user-facing systems.

Build and evolve a high-scale data platform (mondayDB) and end-to-end data applications. Own backend-focused full-stack features, collaborate with product and design, improve architecture, and deliver production-quality user-facing systems.

The Payments Data Analyst will manage the payments data lifecycle, delivering insights to enhance performance, support commercial decisions, and facilitate partnerships across teams while ensuring data quality and accessibility through analytics tools.

Lead data strategy for the Credit Product team, manage data models, collaborate with stakeholders, and maintain high data quality in a fast-paced environment.

As a Senior Data Science Engineer, you'll lead data science projects, develop algorithms, analyze datasets, and mentor junior team members to enhance trading operations and strategies.

Own the data lake product and lead migration to a modern lakehouse. Define roadmap, drive adoption by treating internal teams as customers, onboard workloads, improve performance/cost/quality, and build metadata, ownership and access controls. Track adoption, query performance, costs, and data quality to increase trust and usage.

Lead a small product team owning data foundations, analytics and GenAI foundations. Set strategy, manage and coach product managers (including data lake ownership), prioritize GenAI opportunities, build shared production pipelines (retrieval, orchestration, evaluation, guardrails), define AI evaluation metrics, and partner with Legal/Compliance to embed regulatory requirements.