Top Data Jobs in London

Measure and optimize underwriting using statistical analysis and machine learning on claims, risk signals, and underwriting data. Build models for risk assessment, pricing, and automation; create dashboards and reports; collaborate with actuarial, product, and engineering teams; lead initiatives and mentor team members to improve underwriting efficiency and risk selection.

Lead ownership of private markets data model and architecture, manage integration/connector inventories, design data migration and integration frameworks, advise clients and support sales, and bridge advisory and technical delivery teams to enable BI and enterprise performance solutions.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

Build and maintain high-performance, fault-tolerant distributed backend services and data pipelines for large genomic and health datasets. Design storage, indexing, and async event workflows; optimize latency, throughput, and reliability. Partner with bioinformatics and product teams to validate scientific accuracy and guard AI-driven responses.

The role involves owning product feature development, participating in system design, and maintaining software performance in an agile setting.

As a Senior Data Engineer, you will design and develop large-scale data processing software, manage technical components, and mentor team members, while ensuring code quality and operational readiness.

As a Data Scientist, you will design and deploy data-driven fraud prevention solutions, working directly with clients to tackle unique challenges. You'll build machine learning models and collaborate with engineering to enhance backend systems.

The Data Engineer will design and support data warehouses, create analytics dashboards, perform data quality analysis, and identify process improvements. Responsibilities include collaborating with stakeholders to develop insights and ensure data governance.

Lead analytics strategy and partner with Finance, Sales, Marketing, and Product to design, automate, and maintain metrics, dashboards, and analyses. Ensure data quality (dbt), build scalable analytics solutions, embed AI-driven and agentic workflows, and translate insights into actionable business outcomes to improve acquisition, retention, and growth.

Manage end-to-end multilingual localization and LQA projects: plan scope, allocate resources, track schedules and budgets, prepare briefs and guidelines, onboard reviewers and vendors, operationalize LQA frameworks, run quality checks and calibration, and act as the primary operational client contact to mitigate risks and ensure timely delivery.

Design, build and optimise scalable cloud-native data platforms and pipelines using Microsoft Fabric, Azure Databricks, Synapse and ADF. Implement ETL/ELT with PySpark and SQL, ensure data governance, performance, reliability, and collaborate with data scientists and engineers to deliver production solutions.

The Senior Data Scientist will develop and test deep learning architectures, drive strategy across teams, and ensure models effectively solve real-world financial problems while mentoring others.

The Forward Deployed Data Solution Engineer works embedded within teams to ensure software quality, guide technical decisions, and design integration patterns, focusing on AI-enabled capabilities and data governance.

Design, develop, and maintain ELT/ETL Snowflake data warehouse solutions; implement data standards, CI/CD, monitoring, and production support; collaborate with product, business, and vendors; mentor junior developers and improve data engineering practices.

Lead delivery and ongoing ownership of a modern, cloud-based data platform. Define migration and integration roadmaps, manage programme delivery, embed data governance, support data engineering teams, drive adoption, and optimise reporting, analytics and data quality to enable business decision-making.

Manage the full commercial lifecycle for enterprise and strategic customer accounts: renewals, pricing, contract amendments, upsell strategy, and expansion. Build bespoke commercial strategies using usage data and market context, forecast renewal pipeline, lead commercial QBRs, negotiate with senior stakeholders and procurement, and partner with Customer Success and solution consultants to quantify ROI and drive retained and expanded ARR.

Abstract and enter clinical data from healthcare records, apply clinical operations policies, troubleshoot VPN/MFA access, maintain confidentiality, and provide guidance to less-experienced team members while ensuring data quality and workflow adherence.

Manage client relationships as a subject matter expert, collaborating with Account Executives to drive revenue, engage clients, and provide product insights for innovative solutions.

Design, develop, and deploy machine learning and LLM-based solutions for risk scoring, compliance prediction, churn analysis, and contractor analytics. Integrate models into production on AWS and Azure, use Microsoft Fabric for data pipelines and feature engineering, implement model monitoring and retraining, and collaborate cross-functionally within an Agile SDLC.

Client-facing Principal Data Engineer leading design and delivery of enterprise Databricks-based data platforms. Provide architecture leadership, mentor engineering teams, drive platform optimisation, define standards, support technical governance, and deliver across multiple consultancy engagements.

Lead design and implementation of data governance and MDM strategies, develop policies and metadata/lineage practices, enforce data quality, deploy governance tooling, engage stakeholders, ensure regulatory compliance, and mentor team members.

Design, build, and maintain scalable cloud-based data platform and ETL pipelines on Azure. Develop data models, ensure data quality, optimize performance, implement governance and security, and collaborate with stakeholders and data scientists to enable data-driven solutions.

Translate business needs into technical solutions by collecting, cleaning, and analysing data from multiple sources; build and maintain dashboards and reports; use SQL and Python to automate data processes; ensure data quality; present findings to stakeholders and collaborate across teams to drive process improvements.

Lead and define the data science and AI strategy, deliver production-grade ML and generative AI solutions, operationalise models with MLOps and governance, build and mentor a high-performing team, and influence stakeholders to drive measurable business impact.

Support ledger migration and data lake cleanup by extracting, transforming, reconciling and validating large datasets. Develop and validate SQL queries, batch jobs and processing logic, perform regression testing, document mappings and validation rules, and collaborate with engineering, product and business teams to ensure production readiness and post-migration validation.