Top Data Jobs in London

Design, build, and maintain scalable batch and streaming data pipelines, internal tools, and APIs to support analytics, product features, and ML. Collaborate with product and engineering to improve data infrastructure, ensure reliability, testing, and observability in a cloud-native environment.

As a Data Scientist, you will design and deliver bespoke AI solutions, work with clients on project feasibility, and ensure delivery excellence while acting as a technical advisor.

This role involves analyzing complex datasets, developing metrics, building dashboards, and collaborating with teams to enhance fraud detection strategies using ML models.

Design and validate agentic AI capabilities for Protege in PatentSight using ML, NLP, and LLMs. Build retrieval and hybrid search strategies, run experiments, evaluate models, analyze large IP datasets, and translate results into product recommendations while collaborating with engineers, product, and IP experts.

Design, experiment, and validate NLP/LLM and generative AI solutions for patent intelligence. Develop agentic workflows, retrieval strategies (hybrid semantic+lexical), evaluation metrics, and large-scale text processing methods. Partner with product and engineering to translate validated models into scalable, customer-facing features and deliver measurable business impact.

The Data Engineer will build and scale data pipelines, ensure data quality, implement dbt best practices, and empower cross-functional teams with self-service data access.

Perform data profiling and validation across source and target systems during a large-scale public sector data migration. Define and execute data quality rules, test BI dashboards, write and run SQL (MySQL) including stored procedures, document findings, and collaborate with data engineers to remediate issues while championing data quality across accuracy, completeness, consistency, timeliness, validity, and uniqueness.

Lead marketing measurement: own attribution, incrementality testing, and media mix modeling. Design and analyze experiments, build repeatable templates and self-serve analytics, translate results into budget and growth recommendations, ensure data integrity, and embed AI tools into analysis workflows to measure pipeline and revenue impact.

Lead the data solutions strategy for PR&C PSP division, managing data initiatives, teams, and collaborating on analytics projects. Ensure compliance, governance, and optimize data services while communicating technical solutions to stakeholders.

Own cloud architecture intelligence and cost optimization: analyze cloud spend, identify inefficiencies, build dashboards, enforce governance, partner with engineering/finance to drive cost-conscious design and operations.

The Forward-Deployed Data Analyst will engage with African Ministries of Health, build data management solutions, develop dashboards, and support public health decision-making.

Lead Kraken's Data Platform team focused on real-time infrastructure for data systems, managing team dynamics, architecture design, and promoting AI automation.

The Data Product Analyst designs and manages data products for insurance domains, ensuring they meet analytical and compliance standards while collaborating with cross-functional teams.

As a Lead Data Scientist, you will set technical direction, ensure project delivery, manage client relationships, and mentor data scientists.

Lead the development of backend services for government clients, focusing on high availability and scalability, while collaborating in agile teams and maintaining deployment pipelines.

The Lead Data Architect will shape data strategy, design secure data architectures on AWS, mentor teams, and ensure compliance with regulations.

As a Curriculum Manager, you will manage the content development lifecycle, collaborate with instructors, and improve data science curriculum, ensuring technical accuracy and pedagogical effectiveness.

Lead design, development, and operation of real-time and batch data pipelines and cloud infrastructure. Improve reliability, observability, and data architecture; own features end-to-end and mentor engineering teams while coordinating cross-team stakeholders.

Build, optimise and stabilise AWS-based data pipelines (S3, Glue, Redshift, Lake Formation). Resolve technical debt, support the Architect on target-state design, collaborate with client engineering teams to transfer knowledge, and deliver production-ready platforms in regulated, high-volume gaming environments.

Own and operate Databricks-based data platform production support, build and migrate ETL/ELT pipelines to Databricks across Azure/AWS, optimize compute and performance, enforce Delta Lake architecture and data security, implement observability and CI/CD, support multi-tenant onboarding and on-call incident response, and maintain runbooks and SLA metrics.

As a Senior Data Engineer, you will manage complex data projects, lead small teams, and work with clients on data engineering solutions. Responsibilities include ETL development, data pipeline orchestration, and customer relationship management.

Lead Oyster's data platform and AI strategy, centralize AI initiatives into production systems, build scalable data and MLOps infrastructure, modernize knowledge architecture and semantic search, partner cross-functionally to embed AI into products and workflows, enforce tooling standards and governance, and drive organizational adoption and change management for AI-assisted operations.

The Curriculum Manager will oversee the content development lifecycle, recruit instructors, design curriculum, assess course performance, and improve data science and engineering content.

Lead project teams in delivering bespoke algorithms, mentor other data scientists, and shape client relationships in the defence and national security sector.

Lead discovery, design and delivery of data modernisation and migration projects. Design data lakes/warehouses/lakehouses and landing zones, advise on data strategy, governance and quality, implement Microsoft/Azure and Databricks platforms, support AI/ML initiatives, run workshops, produce technical documentation, contribute reusable assets, and support pre-sales and practice development.