Top Data Jobs in London

Develop and deliver machine learning models (churn, propensity, CLV, recommendations) using large-scale behavioral and transactional data. Translate business problems into modelling solutions, collaborate with engineering, product, and commercial teams, communicate insights to stakeholders, and explore advanced methods (deep learning, sequence models) to drive player engagement and value.

Design, build, and maintain scalable data pipelines and reusable platform components on Azure (Databricks, ADF, ADLS). Drive standardisation, CI/CD, data quality, observability, and performance optimisation while supporting Data Scientists and product teams with tooling, templates, and technical guidance.

Design, implement, support and develop complex machine‑learning models and applications for retail clients. Collaborate with client teams to build scalable, reusable solutions, translate analytics into clear business outcomes, and lead new workstreams from definition through delivery.

Lead a team of data analysts as the maritime SME for vessel and company datasets: ensure data accuracy, design and maintain ETL and ingestion pipelines, implement automation and validation using SQL and Python, integrate new data sources, create dashboards, manage KPIs/OKRs, and collaborate with Product, Engineering, Data, and Commercial teams to deliver high-quality maritime intelligence.

Lead technical strategy for claims fraud data science, designing and shipping production ML and Generative AI/LLM systems. Drive system-level improvements for model integration, monitoring, feedback loops and QA. Influence cross-functional product and engineering decisions to scale AI-driven claims automation and improve measurement of performance and uncertainty.

The Staff Data Scientist will provide technical leadership in Claims, developing ML and AI systems for risk decisioning and fraud detection, collaborating with cross-functional teams to scale decisioning and enhance claims automation.

The Software Engineer will manage data collection for model training, operate cloud infrastructure, and collaborate on data management strategy to enhance Speechify's AI capabilities.

The Data Scientist designs and deploys data models and automation solutions, collaborating with teams to meet business needs through data-driven insights.

The Senior Data Engineer will deliver complex data engineering for insights and analytics, collaborating with multiple stakeholders and managing team outputs.

The Senior/Lead Data Solutions Architect leads data architecture design projects, implements data management frameworks, and collaborates with clients to meet business needs leveraging various data technologies.

The Data Platform Architect will lead complex data engineering projects, act as a trusted advisor to customers, and guide their data platform strategy while documenting architectural designs.

This role involves designing and delivering enterprise-level data solutions using Azure tools to help businesses gain insights from their data repositories.

The role involves building and implementing machine learning models, collaborating with cross-functional teams to solve complex problems in data science.

Build and optimise scalable, serverless data pipelines and storage on GCP. Implement ETL, CI/CD and DataOps practices, support model productionisation with data scientists, document architectures, and guide migration toward event-driven managed cloud services.

Use data to measure reader engagement and content performance, inform newsroom strategy, collaborate with Product and Engineering on experiments, communicate clear insights to stakeholders, and work ethically with data under privacy and governance standards.

Build and lead the Data Labeling team, define annotation guidelines and QA frameworks, partner with researchers to create labeling workflows, prioritize projects, monitor metrics, improve tooling and automation, manage vendors, run complex AI evaluation projects (RLHF, safety, benchmarks), and communicate insights to leadership.

As a Customer Data Solution Architect, you will design secure and scalable customer data solutions, providing thought leadership and ensuring compliance with design principles while collaborating across teams to enhance data strategies and analytics capabilities.

Lead QA&A data analysis: verify and sign off data extracts, ensure GDPR-compliant handling, perform root cause analysis, maintain tools and documentation, improve processes, produce reports, and manage/develop Data Analyst team.

Lead HSE culture and compliance across London projects, implement HSE decisions, advise leadership, manage HSE consultants, develop local HSE programmes, mentor teams, liaise with contractors, and produce structured reports to ensure ISO45001/ISO14001 and statutory requirements are met.

Define and lead AI and data science strategy across ML, NLP, search, recommendation, experimentation, and generative AI. Lead and develop teams to deliver production-ready AI systems (LLMs, RAG, semantic search, embeddings), establish evaluation and responsible AI frameworks, and influence cross-functional stakeholders to align roadmaps and drive measurable product and business outcomes.

Client-facing Enterprise Data Architect designing AI-ready data estates. Assess and modernize data platforms (lakehouse/warehouse), define governance, metadata, semantic models, lineage and data products, advise on platform choices and RAG/vector patterns, and produce target architectures and implementation roadmaps while engaging senior stakeholders.

Lead data architecture and AI-enabled transformation projects for UK insurance clients: define architectures, govern data/AI, run metadata discoveries, assess data assets, support engineering delivery, and drive change management and adoption across multidisciplinary teams.

Lead product strategy and roadmap for data products, coordinate cross-functional teams, translate business needs into scalable data solutions, manage lifecycle from concept to delivery, and track product success using data-driven metrics.

Analyse portfolio performance and credit risk; own analytical projects end-to-end; build forecasting models and monitoring dashboards; present insights to stakeholders; partner cross-functionally to optimise risk strategy, pricing and operations.

Lead a team to operationalise enterprise Master and Reference Data governance: define and implement governance frameworks and processes, drive adoption of standards and tooling, manage stakeholders, produce governance metrics and executive/regulatory reporting, identify process gaps, and ensure alignment with enterprise data policy.