Top Data Jobs in London

Lead high impact data science projects, driving ML model development and deployment while collaborating with cross-functional teams and engaging with stakeholders.

The Data Architect will design data architecture, integrate systems, advise on data governance, and collaborate with stakeholders to deliver solutions.

Lead and scale the SDR team, develop pipeline growth strategies, manage performance metrics, and collaborate with sales and marketing.

Lead end-to-end research projects, delivering insights to inform strategies in marketing and client services, leveraging data for actionable recommendations.

The Data Scientist will develop and manage AI-driven solutions using LLMs, analyze production data, and collaborate across teams to improve AI practices.

As a Data Scientist in Cost Engineering, you'll optimize costs and reduce carbon impact through data-driven insights, collaborating with engineering, procurement, and sustainability teams.

As a Data Engineer, you'll maintain and configure the Snowflake data platform, design integration pipelines, and innovate with new tools. Your role supports data security and performance while collaborating across teams.

The Data Solutions Consultant will engage with decision-makers, provide product demonstrations, and collaborate on innovative marketing strategies to drive sales growth and client satisfaction.

The Junior Data Engineer will analyze data, design experiments, build a marketing data stack, and collaborate with teams to improve data access and insights.

As a Senior Data Engineer, you will design and develop large-scale data processing solutions and lead technical components in agile teams.

As a Data Science Intern, you'll identify frameworks for data-driven business challenges, transform datasets, run analyses, build machine learning models, and communicate results.

PMG seeks interested candidates for future Data & Technology roles. They emphasize professional development, competitive benefits, and a diverse culture.

The Analytics Engineer designs and implements ETL solutions for data integration into Data Lakes/Warehouses, enabling effective data analysis.

As a Senior Data Scientist, you will build and implement machine learning models for credit risk, optimize data use, and mentor junior team members.

The Senior Data Analyst will lead reporting and analytics for Operations teams, develop MI infrastructure, build data models, maintain dashboards, and manage a Senior Analyst.

As a Data Engineer at Helsing, you will lead and automate data acquisition and labeling processes for AI development, support data preparation pipelines, and collaborate with engineering teams.

As Lead Data Scientist, oversee project teams delivering AI solutions, mentor junior team members, and shape technical strategy for financial services clients.

The Data Engineer will design, build, and maintain scalable data infrastructure, ensuring data accessibility for analysis and driving decision-making across the company.

As a Data & AI/ML Architect, you will conduct workshops, oversee project delivery, design architectures, and mentor juniors while engaging with clients.

The Product Data & Attributes Manager will develop product attribute frameworks for e-commerce, ensuring accurate and localised product information across multiple languages and markets.

The Finance & Partnerships Analyst will develop financial models, collaborate with teams, measure partnership performance, analyze revenue impacts, and present insights to influence strategic decisions.

Lead the development of scalable kdb+ solutions for financial institutions, mentoring teams, and designing robust architectures for time-series data.

The role involves developing and maintaining kdb+ applications, collaborating with business users, analyzing time-series data, and mentoring junior developers.

The role involves building and operating a data platform, ensuring data discoverability and query-ability, and mentoring team members.

Lead technical delivery of kdb+ solutions, design robust architectures for time-series data, mentor developers, and drive innovation in a financial context.