1 to 25 of 62 Permanent Model Validation Jobs in the South East

leading hands-on development of models and RAG pipelines to support enterprise-grade LLM applications. MLOps and LLMOps : Build and maintain scalable MLOps and LLMOps pipelines, enabling automation of model training, testing, deployment, and monitoring. Enable reproducibility, traceability, and performance optimization across all AI solutions. Vendor Product Evaluation & Onboarding : Lead the technical evaluation of third-party AI products and … conducting benchmarking, sandbox testing, and assessing integration feasibility. Collaborate with procurement, legal, and security teams to ensure successful onboarding of approved vendors. Governance & Compliance : Define and enforce standards for model validation, bias detection, and explainability, ensuring alignment with internal governance frameworks and external regulations such as GDPR and FCA Platform Enablement: Leverage Databricks, Azure AI, and Microsoft Copilot … while developing reusable AI components, APIs, and Copilot plugins to support business teams and drive adoption.# About You Knowledge Strong understanding of machine learning engineering principles, including pipeline design, model lifecycle management, and production deployment including CI/CD, model registries, and automated workflows while managing feature stores and internal knowledge bases for RAG. Strong grasp of prompt More ❯

gather requirements, design data flows, and help implement pricing models, APIs and analytical capabilities that drive better underwriting decisions. Key Responsibilities: Define and document requirements for pricing system integrations, model deployment and API workflows. Translate pricing models (Excel/Python/R) into technical specifications for developers and data engineers. Collaborate with underwriters and actuaries to improve model governance, version control and reporting. Support UAT, model validation and release testing. Deliver insights and metrics around model performance, quote success and underwriting efficiency. Partner with insurers and MGAs to streamline pricing and underwriting processes through automation and data. Required Skills & Experience: 3+ years in a Business Analyst or Technical Analyst role within insurance, reinsurance or … InsurTech. Experience with pricing or rating systems, underwriting workflows or model integration projects. Strong understanding of insurance data structures, rating factors and model governance. Skilled in SQL and BI tools (Power BI, Tableau, Looker). Comfortable working with APIs, data pipelines and collaborating with developers. Excellent communication and stakeholder management skills. Desirable: Exposure to cloud environments (AWS, Azure More ❯

gather requirements, design data flows, and help implement pricing models, APIs and analytical capabilities that drive better underwriting decisions. Key Responsibilities: Define and document requirements for pricing system integrations, model deployment and API workflows. Translate pricing models (Excel/Python/R) into technical specifications for developers and data engineers. Collaborate with underwriters and actuaries to improve model governance, version control and reporting. Support UAT, model validation and release testing. Deliver insights and metrics around model performance, quote success and underwriting efficiency. Partner with insurers and MGAs to streamline pricing and underwriting processes through automation and data. Required Skills & Experience: 3+ years in a Business Analyst or Technical Analyst role within insurance, reinsurance or … InsurTech. Experience with pricing or rating systems, underwriting workflows or model integration projects. Strong understanding of insurance data structures, rating factors and model governance. Skilled in SQL and BI tools (Power BI, Tableau, Looker). Comfortable working with APIs, data pipelines and collaborating with developers. Excellent communication and stakeholder management skills. Desirable: Exposure to cloud environments (AWS, Azure More ❯

gather requirements, design data flows, and help implement pricing models, APIs and analytical capabilities that drive better underwriting decisions. Key Responsibilities: Define and document requirements for pricing system integrations, model deployment and API workflows. Translate pricing models (Excel/Python/R) into technical specifications for developers and data engineers. Collaborate with underwriters and actuaries to improve model governance, version control and reporting. Support UAT, model validation and release testing. Deliver insights and metrics around model performance, quote success and underwriting efficiency. Partner with insurers and MGAs to streamline pricing and underwriting processes through automation and data. Required Skills & Experience: 3+ years in a Business Analyst or Technical Analyst role within insurance, reinsurance or … InsurTech. Experience with pricing or rating systems, underwriting workflows or model integration projects. Strong understanding of insurance data structures, rating factors and model governance. Skilled in SQL and BI tools (Power BI, Tableau, Looker). Comfortable working with APIs, data pipelines and collaborating with developers. Excellent communication and stakeholder management skills. Desirable: Exposure to cloud environments (AWS, Azure More ❯

role centers on evaluating analytical workflows, modeling standards, experimentation culture, and applied business impact . This position is ideal for someone with a strong background in applied data science, model lifecycle design, and organizational data maturity capable of analyzing current practices and defining what best-in-class looks like for scalable, responsible, and high-impact data science operations. Key … Responsibilities Practice Maturity Assessment: Evaluate current data science processes, tools, and team structures to determine capability strengths, weaknesses, and improvement areas. Framework Design: Develop and apply a structured maturity model to assess how data science work is conceived, executed, validated, and scaled. Model Lifecycle Review: Assess practices across data preparation, feature engineering, model development, validation, monitoring … . Collaboration & Alignment: Work with AI and Data & AI Architects to connect findings from people, platform, and practice assessments into a unified capability map. Gap Identification: Identify gaps in model governance, documentation, and model-to-business translation and recommend actionable improvement pathways. Reporting & Advisory: Produce detailed reports summarizing data science maturity, practice gaps, and recommendations for scaling responsibly More ❯

role centers on evaluating analytical workflows, modeling standards, experimentation culture, and applied business impact . This position is ideal for someone with a strong background in applied data science, model lifecycle design, and organizational data maturity capable of analyzing current practices and defining what best-in-class looks like for scalable, responsible, and high-impact data science operations. Key … Responsibilities Practice Maturity Assessment: Evaluate current data science processes, tools, and team structures to determine capability strengths, weaknesses, and improvement areas. Framework Design: Develop and apply a structured maturity model to assess how data science work is conceived, executed, validated, and scaled. Model Lifecycle Review: Assess practices across data preparation, feature engineering, model development, validation, monitoring … . Collaboration & Alignment: Work with AI and Data & AI Architects to connect findings from people, platform, and practice assessments into a unified capability map. Gap Identification: Identify gaps in model governance, documentation, and model-to-business translation and recommend actionable improvement pathways. Reporting & Advisory: Produce detailed reports summarizing data science maturity, practice gaps, and recommendations for scaling responsibly More ❯

role centers on evaluating analytical workflows, modeling standards, experimentation culture, and applied business impact . This position is ideal for someone with a strong background in applied data science, model lifecycle design, and organizational data maturity capable of analyzing current practices and defining what best-in-class looks like for scalable, responsible, and high-impact data science operations. Key … Responsibilities Practice Maturity Assessment: Evaluate current data science processes, tools, and team structures to determine capability strengths, weaknesses, and improvement areas. Framework Design: Develop and apply a structured maturity model to assess how data science work is conceived, executed, validated, and scaled. Model Lifecycle Review: Assess practices across data preparation, feature engineering, model development, validation, monitoring … . Collaboration & Alignment: Work with AI and Data & AI Architects to connect findings from people, platform, and practice assessments into a unified capability map. Gap Identification: Identify gaps in model governance, documentation, and model-to-business translation and recommend actionable improvement pathways. Reporting & Advisory: Produce detailed reports summarizing data science maturity, practice gaps, and recommendations for scaling responsibly More ❯

role centers on evaluating analytical workflows, modeling standards, experimentation culture, and applied business impact . This position is ideal for someone with a strong background in applied data science, model lifecycle design, and organizational data maturity - capable of analyzing current practices and defining what "best-in-class" looks like for scalable, responsible, and high-impact data science operations. Key … Responsibilities • Practice Maturity Assessment: Evaluate current data science processes, tools, and team structures to determine capability strengths, weaknesses, and improvement areas. • Framework Design: Develop and apply a structured maturity model to assess how data science work is conceived, executed, validated, and scaled. • Model Lifecycle Review: Assess practices across data preparation, feature engineering, model development, validation, monitoring … . • Collaboration & Alignment: Work with AI and Data & AI Architects to connect findings from people, platform, and practice assessments into a unified capability map. • Gap Identification: Identify gaps in model governance, documentation, and model-to-business translation and recommend actionable improvement pathways. • Reporting & Advisory: Produce detailed reports summarizing data science maturity, practice gaps, and recommendations for scaling responsibly More ❯

role centers on evaluating analytical workflows, modeling standards, experimentation culture, and applied business impact . This position is ideal for someone with a strong background in applied data science, model lifecycle design, and organizational data maturity — capable of analyzing current practices and defining what “best-in-class” looks like for scalable, responsible, and high-impact data science operations. Key … Responsibilities • Practice Maturity Assessment: Evaluate current data science processes, tools, and team structures to determine capability strengths, weaknesses, and improvement areas. • Framework Design: Develop and apply a structured maturity model to assess how data science work is conceived, executed, validated, and scaled. • Model Lifecycle Review: Assess practices across data preparation, feature engineering, model development, validation, monitoring … . • Collaboration & Alignment: Work with AI and Data & AI Architects to connect findings from people, platform, and practice assessments into a unified capability map. • Gap Identification: Identify gaps in model governance, documentation, and model-to-business translation and recommend actionable improvement pathways. • Reporting & Advisory: Produce detailed reports summarizing data science maturity, practice gaps, and recommendations for scaling responsibly More ❯

role centers on evaluating analytical workflows, modeling standards, experimentation culture, and applied business impact . This position is ideal for someone with a strong background in applied data science, model lifecycle design, and organizational data maturity — capable of analyzing current practices and defining what “best-in-class” looks like for scalable, responsible, and high-impact data science operations. Key … Responsibilities • Practice Maturity Assessment: Evaluate current data science processes, tools, and team structures to determine capability strengths, weaknesses, and improvement areas. • Framework Design: Develop and apply a structured maturity model to assess how data science work is conceived, executed, validated, and scaled. • Model Lifecycle Review: Assess practices across data preparation, feature engineering, model development, validation, monitoring … . • Collaboration & Alignment: Work with AI and Data & AI Architects to connect findings from people, platform, and practice assessments into a unified capability map. • Gap Identification: Identify gaps in model governance, documentation, and model-to-business translation and recommend actionable improvement pathways. • Reporting & Advisory: Produce detailed reports summarizing data science maturity, practice gaps, and recommendations for scaling responsibly More ❯

role centers on evaluating analytical workflows, modeling standards, experimentation culture, and applied business impact . This position is ideal for someone with a strong background in applied data science, model lifecycle design, and organizational data maturity - capable of analyzing current practices and defining what "best-in-class" looks like for scalable, responsible, and high-impact data science operations. Key … Responsibilities • Practice Maturity Assessment: Evaluate current data science processes, tools, and team structures to determine capability strengths, weaknesses, and improvement areas. • Framework Design: Develop and apply a structured maturity model to assess how data science work is conceived, executed, validated, and scaled. • Model Lifecycle Review: Assess practices across data preparation, feature engineering, model development, validation, monitoring … . • Collaboration & Alignment: Work with AI and Data & AI Architects to connect findings from people, platform, and practice assessments into a unified capability map. • Gap Identification: Identify gaps in model governance, documentation, and model-to-business translation and recommend actionable improvement pathways. • Reporting & Advisory: Produce detailed reports summarizing data science maturity, practice gaps, and recommendations for scaling responsibly More ❯

role centers on evaluating analytical workflows, modeling standards, experimentation culture, and applied business impact . This position is ideal for someone with a strong background in applied data science, model lifecycle design, and organizational data maturity — capable of analyzing current practices and defining what “best-in-class” looks like for scalable, responsible, and high-impact data science operations. Key … Responsibilities • Practice Maturity Assessment: Evaluate current data science processes, tools, and team structures to determine capability strengths, weaknesses, and improvement areas. • Framework Design: Develop and apply a structured maturity model to assess how data science work is conceived, executed, validated, and scaled. • Model Lifecycle Review: Assess practices across data preparation, feature engineering, model development, validation, monitoring … . • Collaboration & Alignment: Work with AI and Data & AI Architects to connect findings from people, platform, and practice assessments into a unified capability map. • Gap Identification: Identify gaps in model governance, documentation, and model-to-business translation and recommend actionable improvement pathways. • Reporting & Advisory: Produce detailed reports summarizing data science maturity, practice gaps, and recommendations for scaling responsibly More ❯

Job Title: Data Scientist (Contract, Hybrid – London) Location: London (Hybrid: 2–3 days/week in office) Contract Type 6–12 months (Contract, possible extension) About The Role We are seeking a skilled Data Scientist to join our dynamic team More ❯

migration projects, ensuring data accuracy and completeness. Design, develop, and maintain dashboards and reports using Power BI (experience with Tableau is a plus). Perform data modeling, mapping, and validation to support business intelligence and reporting requirements. Communicate complex technical concepts effectively to both technical and non-technical stakeholders. Collaborate with cross-functional teams to identify opportunities for process More ❯

migration projects, ensuring data accuracy and completeness. Design, develop, and maintain dashboards and reports using Power BI (experience with Tableau is a plus). Perform data modeling, mapping, and validation to support business intelligence and reporting requirements. Communicate complex technical concepts effectively to both technical and non-technical stakeholders. Collaborate with cross-functional teams to identify opportunities for process More ❯

migration projects, ensuring data accuracy and completeness. Design, develop, and maintain dashboards and reports using Power BI (experience with Tableau is a plus). Perform data modeling, mapping, and validation to support business intelligence and reporting requirements. Communicate complex technical concepts effectively to both technical and non-technical stakeholders. Collaborate with cross-functional teams to identify opportunities for process More ❯

migration projects, ensuring data accuracy and completeness. Design, develop, and maintain dashboards and reports using Power BI (experience with Tableau is a plus). Perform data modeling, mapping, and validation to support business intelligence and reporting requirements. Communicate complex technical concepts effectively to both technical and non-technical stakeholders. Collaborate with cross-functional teams to identify opportunities for process More ❯

Provide ongoing system support, resolve issues, and perform debugging and testing. • Ensure platform stability and security across multiple environments. Data Management & Quality • Design and manage data models, queries, and validation rules to ensure integrity and reliability. • Support data migration and ensure compliance with governance standards. Collaboration & Documentation • Work closely with stakeholders, project managers, and other IT specialists to translate More ❯

Provide ongoing system support, resolve issues, and perform debugging and testing. • Ensure platform stability and security across multiple environments. Data Management & Quality • Design and manage data models, queries, and validation rules to ensure integrity and reliability. • Support data migration and ensure compliance with governance standards. Collaboration & Documentation • Work closely with stakeholders, project managers, and other IT specialists to translate More ❯

Provide ongoing system support, resolve issues, and perform debugging and testing. • Ensure platform stability and security across multiple environments. Data Management & Quality • Design and manage data models, queries, and validation rules to ensure integrity and reliability. • Support data migration and ensure compliance with governance standards. Collaboration & Documentation • Work closely with stakeholders, project managers, and other IT specialists to translate More ❯

a global Lakehouse platform (Data Lake + OLAP) in AWS Develop scalable data pipelines in Python , working with SDKs and data libs Own end-to-end data modeling , ingestion, validation, and optimization for high-concurrency access Tune performance of cross-region, multi-format data stores (columnar, real-time, etc.) Deliver tailored solutions directly to quants and traders - real impact More ❯

a global Lakehouse platform (Data Lake + OLAP) in AWS Develop scalable data pipelines in Python , working with SDKs and data libs Own end-to-end data modeling , ingestion, validation, and optimization for high-concurrency access Tune performance of cross-region, multi-format data stores (columnar, real-time, etc.) Deliver tailored solutions directly to quants and traders real impact More ❯

a global Lakehouse platform (Data Lake + OLAP) in AWS Develop scalable data pipelines in Python , working with SDKs and data libs Own end-to-end data modeling , ingestion, validation, and optimization for high-concurrency access Tune performance of cross-region, multi-format data stores (columnar, real-time, etc.) Deliver tailored solutions directly to quants and traders – real impact More ❯

a global Lakehouse platform (Data Lake + OLAP) in AWS Develop scalable data pipelines in Python , working with SDKs and data libs Own end-to-end data modeling , ingestion, validation, and optimization for high-concurrency access Tune performance of cross-region, multi-format data stores (columnar, real-time, etc.) Deliver tailored solutions directly to quants and traders real impact More ❯

requirements. Develop process maps and target operating models to enhance clarity and efficiency. Data analysis and definition, e.g. data-led insights, development ofdata dictionaries and data models. Supporting the validation of solutions delivered against acceptance criteria, through test planning, execution, coordination, etc. Partnering with Architects or Development teams to produce appropriate design specifications Continuously review and assess solution delivery More ❯