Top Data Jobs in London

Lead delivery of technology, data platforms, and analytics across the investment lifecycle. Drive strategy execution, enterprise prioritisation, governance, and value realisation to enhance investment decision-making, client engagement, and commercial growth within a regulated asset management environment.

Lead the data science organization to deliver AI products, mentoring teams and executing a strategic vision for machine learning applications.

The Senior Data Platform Engineer will architect and build a scalable data platform, enabling teams to manage data with self-serve capabilities using modern tools. Responsibilities include developing microservices, leading technical initiatives, and promoting data governance and observability, collaborating with diverse stakeholders, and mentoring team members.

Design and deliver scalable cloud data pipelines, ETL/ELT and data warehousing solutions. Work hands-on in Snowflake, Databricks and cloud platforms, translate business requirements into technical designs, optimise queries, and engage with stakeholders across client projects and internal initiatives.

As the first data hire, you'll establish analytics to enhance product usage understanding and business decisions, while creating reports and models for retention and sales analysis.

As a Lead Data Engineer, you will design and develop software for data processing, lead a team, ensure quality, and manage technical proposals.

The Senior Data Analyst will own analytics for finance, create dashboards, ensure data quality, and communicate insights to support decision-making.

The Senior Data Scientist will build and maintain models for simulating the logistics network, addressing operational queries from finance teams about costs and efficiencies.

Senior leadership role accountable for delivering technology, data and analytics across the investment lifecycle. Owns data platforms, data engineering, analytics enablement and enterprise prioritisation, governance and value realisation to enhance investment decision-making, client engagement and commercial growth within a regulated financial services environment.

As a Senior Data Platform Engineer, you will design and operate a scalable data platform, support analytics, mentor junior engineers, and drive technical excellence.

The Principal Data Scientist will lead complex projects, drive innovation in AI solutions, mentor teams, and influence data science strategy.

As a kdb+ Developer, you'll work on trade surveillance projects, collaborating with business users, developing client applications, mentoring junior developers, and analyzing data to create client reports.

Lead cross-functional initiatives to enhance KOL data products, translating customer needs into development initiatives and ensuring data quality and usability.

The Principal Architect will lead the design and strategy of next-generation data center architecture, focusing on software-defined infrastructure, workload management, and governance, with extensive experience required in various technologies.

The VP, Head of Policy & Data Operations manages data governance and operations for proxy voting policies, ensuring data integrity and operational excellence while collaborating across teams to optimize processes and manage risks.

Transform procurement, cloud and infrastructure data into dashboards, forecasts and insights to support capacity planning, supplier management, cost optimisation and investment decisions. Build ETL pipelines, KPI frameworks and automated reporting, partnering with procurement, finance, engineering and operations and presenting findings to senior stakeholders.

Lead technical design and delivery of advanced AI solutions (ML, generative and agentic AI), drive adoption of modern AI engineering and cloud-native architectures, ensure responsible AI practices, mentor and manage a small team, and liaise with clients to translate business needs into production-grade, secure AI systems.

Design, build and optimise ETL/ELT pipelines and data foundations across enterprise systems. Profile, cleanse, map and reconcile data, implement quality frameworks, develop scalable data models on cloud platforms, and work with stakeholders to establish repeatable data architecture, governance and reporting for transformation programmes.

Lead and grow Almedia's London data and ML team (5–10 hires), stay hands-on with code and model reviews, own roadmap for recommendation, personalization and offer-ranking systems, hire and coach staff, and align technical work to business outcomes with senior leadership.

Design, develop, and deploy scalable ML and time-series forecasting engines integrated into Anaplan applications. Lead customer onboarding and customize models to client data, build feature pipelines (SQL/Python/dbt), and operationalize models via MLOps tools (MLflow, Optuna). Collaborate with Product, Engineering, and Customer Success to deliver production-grade, monitored solutions.

Design and evolve a scalable, multi-tenant cloud data platform prioritizing stability, fault tolerance, cost efficiency, developer productivity, and security-by-design. Define architecture standards, review designs, modernize legacy components incrementally, implement monitoring and observability, prototype new technologies, and provide technical guidance so teams can ship reliably and quickly.

Design and govern end-to-end data architectures and standards, guide technology selection, lead large data migrations to cloud-native platforms, enable real-time data services, mentor engineers, and ensure secure, scalable data solutions that support AI/ML workloads.

Lead causal inference and econometric analysis to measure impact of pricing and product changes. Design experiments and quasi-experiments, build robust metrics with production-quality SQL and Python, communicate results to non-technical stakeholders, and use AI tools to accelerate analysis while maintaining research integrity.

Lead causal inference and econometric analysis to measure impacts of pricing, products, and policy. Define experimental and quasi-experimental standards, design structural models, and build scalable metric trees using optimized SQL and Python. Partner with business leaders, deliver executive-grade narratives about statistical uncertainty, and establish verification frameworks for safe AI-assisted analysis.

Design, build, and maintain scalable ML models and optimization systems for production. Own end-to-end projects, implement numerical optimizers (LP/MIP), write production-grade Python and SQL on Databricks, monitor drift, and mentor peers to ensure robust, interpretable, and maintainable ML services.