26 to 50 of 386 Use Case Jobs in London

the development and implementation of AI solutions across portfolio companies to drive operational efficiency and growth. Collaborate with internal teams and portfolio company management to identify high-impact use cases for AI tools. Oversee AI/ML model development, deployment, and monitoring, ensuring scalability and robustness. Conduct data analysis, experimentation, and validation to continuously improve AI systems and More ❯

the development and implementation of AI solutions across portfolio companies to drive operational efficiency and growth. Collaborate with internal teams and portfolio company management to identify high-impact use cases for AI tools. Oversee AI/ML model development, deployment, and monitoring, ensuring scalability and robustness. Conduct data analysis, experimentation, and validation to continuously improve AI systems and More ❯

major insurance client engagement . The role focuses on scaling a Snowflake Data Warehouse and expanding its DBT Cloud modelling capabilities to support new analytics and regulatory data use cases across the business. Assignment Details Initial Duration: 6-12 months (until March 2026, likely extension given scope) Location: Hybrid - minimum of 2 days per week in London Day More ❯

engagement to close as needed Identify and understand customer requirements and articulate the value proposition of Palo Alto Networks’ AI security and Software and Cloud Firewall offerings and use cases to meet those requirements. Evangelize the importance and value of securing AI across industry events, executive briefings, and thought leadership opportunities Track and report performance metrics, pipeline health More ❯

datasets from multiple sources (investment, risk, client, operational) to create integrated analytical views. Contribute to the development of a modern data platform supporting advanced analytics and machine learning use cases. Support the migration of legacy reporting tools into Databricks and modern BI solutions. Key Skills & Experience Essential: Strong hands-on experience with Databricks (SQL, PySpark, Delta Lake). More ❯

datasets from multiple sources (investment, risk, client, operational) to create integrated analytical views. Contribute to the development of a modern data platform supporting advanced analytics and machine learning use cases. Support the migration of legacy reporting tools into Databricks and modern BI solutions. Key Skills & Experience Essential: Strong hands-on experience with Databricks (SQL, PySpark, Delta Lake). More ❯

maintain data pipelines and data integration solutions using Azure Data Factory, Synapse Analytics, and related services. Build and optimise data models to support reporting, analytics, and machine learning use cases. Implement robust data quality, governance, and security practices across the data ecosystem. Collaborate with analysts, data scientists, and business stakeholders to understand data needs and translate them into More ❯

team of data scientists and collaborating with engineering and product leaders. Key Responsibilities Drive end-to-end delivery of data science solutions for digital banking and risk analytics use cases. Build predictive models for credit risk, fraud prevention, and customer behaviour using cutting-edge ML techniques. Partner with product teams to embed intelligent decision engines into core banking More ❯

team of data scientists and collaborating with engineering and product leaders. Key Responsibilities Drive end-to-end delivery of data science solutions for digital banking and risk analytics use cases. Build predictive models for credit risk, fraud prevention, and customer behaviour using cutting-edge ML techniques. Partner with product teams to embed intelligent decision engines into core banking More ❯

AI-driven experiences and enabling digital transformation across the organisation. Key Responsibilities Lead the design, configuration, and deployment of Oracle Digital Assistant (ODA) and Oracle Intelligent Advisor (OIA) use cases. Develop and implement AI Agent solutions leveraging Oracle Redwood and associated AI frameworks. Collaborate with business stakeholders to identify and deliver impactful AI-enabled business solutions. Provide technical More ❯

Azure and GCP. As a Full Stack Commerce Engineer Manager, you will : Lead Client-Facing Engagements : Act as the primary technical authority in client workshops, designing use-case and platform technology solutions that use Generative AI to solve their key business challenges. Architect & Build AI-Powered Solutions : Design and build complex, full-stack solutions from More ❯

Company Market leading SaaS organisation building out their AI function. About the Role This project is a greenfield build out of AI capabilities. They are starting with RAG use cases as a low bar, but are targeting a rich Agentic implementation in the near term. The incumbent has an opportunity to work on the foundational architectural definition and More ❯

Company Market leading SaaS organisation building out their AI function. About the Role This project is a greenfield build out of AI capabilities. They are starting with RAG use cases as a low bar, but are targeting a rich Agentic implementation in the near term. The incumbent has an opportunity to work on the foundational architectural definition and More ❯

major insurance client engagement . The role focuses on scaling a Snowflake Data Warehouse and expanding its DBT Cloud modelling capabilities to support new analytics and regulatory data use cases across the business. Assignment Details Initial Duration: 6–12 months (until March 2026, likely extension given scope) Location: Hybrid – minimum of 2 days per week in London Day More ❯

major insurance client engagement . The role focuses on scaling a Snowflake Data Warehouse and expanding its DBT Cloud modelling capabilities to support new analytics and regulatory data use cases across the business. Assignment Details Initial Duration: 6–12 months (until March 2026, likely extension given scope) Location: Hybrid – minimum of 2 days per week in London Day More ❯

highest level. Drive the development and adoption of scalable, automated data science tooling and frameworks. Explore and integrate emerging AI technologies, such as LLMs and Agentic AI, taking use cases from proof-of-concept through to production. Champion best practices in experimentation, causal inference, and uplift modelling. Own and deliver the most complex analytical projects, translating ambiguous questions More ❯

highest level. Drive the development and adoption of scalable, automated data science tooling and frameworks. Explore and integrate emerging AI technologies, such as LLMs and Agentic AI, taking use cases from proof-of-concept through to production. Champion best practices in experimentation, causal inference, and uplift modelling. Own and deliver the most complex analytical projects, translating ambiguous questions More ❯

requirements for new system integrations. Work with stakeholders to define integration points between rights management tools, MAM systems, data platforms, and finance systems. Translate business needs into integration use cases, data mapping, and interface specifications. Document end-to-end process flows, service journeys, and hand-offs across business and technology teams. Act as a bridge between business, technical More ❯

support strategic decision-making for clients, utilising data visualisation tools and techniques to present findings effectively. Lead Impactful Discussions: Facilitate workshops and discussions with clients to explore innovative use cases for Data & AI, fostering collaborative ideation and problem-solving. Contribute to Practice Development: Actively participate in the development of point of views, thought leadership and intellectual property within More ❯

support strategic decision-making for clients, utilising data visualisation tools and techniques to present findings effectively. Lead Impactful Discussions: Facilitate workshops and discussions with clients to explore innovative use cases for Data & AI, fostering collaborative ideation and problem-solving. Drive Practice Development: Lead the development of point of views, thought leadership and intellectual property within the Data & AI More ❯

engagement to close as needed Identify and understand customer requirements and articulate the value proposition of Palo Alto Networks’ AI security and Software and Cloud Firewall offerings and use cases to meet those requirements. Evangelize the importance and value of securing AI across industry events, executive briefings, and thought leadership opportunities Track and report performance metrics, pipeline health More ❯

ingestion pipelines and transformations within the Quantexa platform using Spark and Scala. Configure and implement Quantexa components such as Entity Resolution, Scoring, and Network Generation to support specific use cases. Collaborate with Tech Leads and Solution Architects to design scalable and performant Quantexa solutions. Translate business and technical requirements into efficient, production-ready data engineering solutions. Support the More ❯

ingestion pipelines and transformations within the Quantexa platform using Spark and Scala. Configure and implement Quantexa components such as Entity Resolution, Scoring, and Network Generation to support specific use cases. Collaborate with Tech Leads and Solution Architects to design scalable and performant Quantexa solutions. Translate business and technical requirements into efficient, production-ready data engineering solutions. Support the More ❯

Working knowledge of data modelling, views, and performance tuning. SQL: Proficient in writing, optimising, and troubleshooting queries. Azure Data Lake: Understanding of data storage and structure for analytics use cases. Git/Azure DevOps: Familiar with version control and Agile delivery workflows. Microsoft Fabric (Desirable): Awareness or early exposure to Fabric data pipelines, dataflows, and workspace management. Soft More ❯

Working knowledge of data modelling, views, and performance tuning. SQL: Proficient in writing, optimising, and troubleshooting queries. Azure Data Lake: Understanding of data storage and structure for analytics use cases. Git/Azure DevOps: Familiar with version control and Agile delivery workflows. Microsoft Fabric (Desirable): Awareness or early exposure to Fabric data pipelines, dataflows, and workspace management. Soft More ❯