Top Data Jobs in London

Lead product strategy and delivery for Aladdin's wealth management technology. Define roadmap, embed AI and API-led integrations, drive cross-functional squads, engage clients and partners, and deliver scalable solutions for discretionary portfolios, advisory services, execution-only accounts, model management, trading, and reporting.

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.

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.

Leads Zeta’s UK enterprise customer-growth strategy across retention, expansion, executive relationships, and delivery. Builds and manages a Key Account Director organization, establishes strategic account plans, coordinates cross-functional teams, identifies expansion opportunities, manages customer risk, and improves revenue retention and product adoption. The role develops repeatable customer-growth playbooks, strengthens executive engagement, and scales the model across EMEA.

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.

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

Develops data science and analytics solutions for Global Payments by exploring transaction flows, identifying high-impact use cases, delivering actionable insights, and supporting data-driven product strategy. Collaborates with stakeholders and product managers, translates complex data requirements into analytical solutions, promotes data products, and contributes to robust data assets and product enhancements.

Build and optimize Lakehouse data pipelines (Trino + Iceberg) using Medallion Architecture to transform high-throughput event logs into audit-ready subledgers and reporting metrics. Implement stream processing with Kafka, embed automated reconciliation/data quality checks, collaborate with finance and product stakeholders, and mentor junior engineers.

Support investment teams by acquiring, cleaning, and analyzing multi-source data to test investment theses, identify inflection points, and present research. Learn fundamental investor approaches, build decision-support tools, and communicate findings clearly to Sector Data Analysts and investment teams.

Lead complex data analytics initiatives for top financial institutions, utilizing predictive modeling and creating dashboards. Support innovation in AI and machine learning.

Partners with product teams to gather requirements, analyze data, create reports, and develop data-driven solutions. Designs data models, maintains data quality, improves reporting through automation, resolves data discrepancies, supports testing and investigations, communicates insights through visualizations, and contributes to data governance and regulatory compliance.

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.

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 architecture and cloud transformation engagements for financial services clients. Define enterprise data strategy, design scalable cloud-native data platforms, integrate governance, cataloguing, privacy and metadata capabilities, and present technical solutions to stakeholders while shaping Capco's data architecture offerings.

Design scalable telemetry and event schemas for game analytics. Partner with game dev, analytics engineering, and data science to translate requirements into telemetry, validation rules, and documentation. Ensure data quality, support QA checks, and maintain standards for discoverability, governance, and downstream modelling, reporting, experimentation, and ML use.

Design, prototype, implement and optimize predictive models for sporting outcomes. Collaborate in an agile, multidisciplinary team, apply probability and machine learning methods, deploy models, and mentor junior colleagues.

Lead and manage a team of data scientists to design, build and deploy predictive sports models. Oversee algorithm development (probability, Monte Carlo, ML), mentor staff, collaborate with engineers and product managers, and ensure continuous delivery and high-performance, production-ready model implementations across multiple sports.

Lead and mentor data science teams to design, develop, and deploy ML and AI solutions (including GenAI/LLMs). Align technical outcomes with business goals, drive MLOps and model governance, and deliver scalable production systems.

Support data and customer-success functions by entering, cleaning, and analyzing data; maintain databases; assist clients with LevPro configuration, demos, onboarding, and implementation; identify process improvements and help build client relationships.

Build secure, scalable AI/ML platforms and applications to productionize research. Collaborate with AI researchers and data scientists, implement CI/CD and IaC, support experimentation environments, automate remediation, mentor junior engineers, and promote secure, responsible AI development practices.

Develop, implement, and deploy statistical and machine learning sports models and pricing engines in Python; engineer sports data assets; build automated tests; collaborate with Trading, Product, Engineering, and QA; validate data flows and integrations; and mentor junior data scientists.

Own end-to-end regulatory reporting datasets: map regulations to internal data, build and automate pipelines, implement data quality controls, create clear visualisations, investigate discrepancies, and partner with cross-functional stakeholders to ensure accurate, auditable reporting.

Lead analytics for FinCrime operations: own forecasting and capacity planning, maintain data pipelines, build predictive and causal models, assess operational health and costs, implement KPI frameworks, track initiative performance, and standardise processes across forecasting, scheduling, and real-time operations.

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.