76 to 100 of 373 Model Validation Jobs in the UK

users. Key responsibilities Lead the scoping, design and delivery of complex data science products, models and analytical solutions. Set standards for modelling, validation, reproducibility, coding, documentation and responsible AI practices. Identify opportunities to apply machine learning, predictive analytics, optimisation or advanced statistics to business challenges. Guide data scientists, analysts … science practice. Essential skills and experience Extensive experience designing and delivering advanced data science solutions at scale. Deep understanding of applied statistics, machine learning, model validation and data science product delivery. Strong programming capability and ability to set coding, testing and reproducibility standards. Experience working with data engineering ...

features and analytical outputs through approved Fabric and Databricks data products, tables, APIs, search indexes, batch pipelines or product features. Maintain reproducible code, tests, model and data documentation, evaluation evidence and clear statements of limitations and appropriate use. Use AI agents and coding assistants responsibly to accelerate exploratory work … quality measures. Develop reusable pipelines in Microsoft Fabric and Databricks for entity resolution, enrichment, metadata, relationship mapping and data-quality monitoring. Run EDA and model development that increases the value of priority data assets for client intelligence, opportunity identification, pricing, matter analysis and other high-value use cases. What ...

example, Docker, FastAPI). Strong SQL, a solid grounding in relational databases, schemas and data transformations (ideally DBT), and experience preparing data for model training and scoring. Hands‐on AI experience: embeddings, vector databases or RAG workflows, and NLP or sequence models applied to real-world datasets. Experience delivering ...

example, Docker, FastAPI). Strong SQL, a solid grounding in relational databases, schemas and data transformations (ideally DBT), and experience preparing data for model training and scoring. Hands-on AI experience: embeddings, vector databases or RAG workflows, and NLP or sequence models applied to real-world datasets. Experience delivering ...

toolset. Experienceworking in an Agile software development framework. Excellentworking knowledge of Data Build Tool (DBT) with a demonstrated ability indeveloping data models, contracts, tests, validation, and transformations Experienceworking with modern data distributed file formats (i.e., Parquet, Delta,Iceberg, Hudi) Demonstratedexperience with building data ingestion pipelines from REST API data ...

insights. Apply appropriate machine learning, predictive analytics and data science techniques to real-world problems. Develop and evaluate insightful models using robust performance and validation methodologies. Communicate findings and recommendations clearly to both technical and non-technical stakeholders. Contribute to development of innovative AI, machine learning and advanced analytics … analyse large-scale datasets. Develop and manage machine learning workflows within an MLOps environment. Use tooling such as MLflow, VS Code or JupyterHub for model development, experimentation and deployment. Work with technologies to access, manage and analyse data at scale (e.g., Apache Iceberg and Trino). Delivery Excellence Participate ...

notebooks, Delta tables, workflows and scheduled analytical pipelines. Working proficiency with Git/Azure DevOps, pull requests and controlled production changes. Strong data-validation, reconciliation and audit-trail practices. Nice to have: Loyalty economics, retail fuel/convenience or CRM-domain experience. Power BI or certified reporting-layer experience. … Production pipeline design beyond analytical notebooks, including Delta MERGE, idempotency and incremental processing. Experimentation or uplift-model validation experience. BNA or customer-decisioning exposure. MUST HAVE high-volume customer, payment, loyalty and transaction analysis Data Scientist Role Overview: Lead Data Scientist - Customer Intelligence, Incrementality & Value Measurement Primary focus ...

Moody's Investors Service is seeking a senior AI/ML model risk professional to independently evaluate AI-enabled tools, agents and quantitative models used in credit rating activities. You will strengthen GenAI and agentic AI governance with rigorous validation, testing, and reporting. The role emphasizes production readiness ...

collaborative development, deployment and professional practice. Statistics, data science and artificial intelligence: probability, statistical inference, regression, data preparation, data visualisation, supervised and unsupervised learning, model validation and interpretation, and critical consideration of contemporary AI, including generative AI, large language models and AGI. Professional and responsible computing: accessibility ...

ICBs and support health systems across the UK, Ireland and the DACH region to reduce hospital pressure, improve outcomes, and create a more resilient model of care. We're backed by top European investors, having secured £35m in Series B funding led by Lakestar, with participation from Elaia, General ...

insights grounded in trusted data and established metrics. About the role: We are seeking a Senior Data Scientist I to help the design and validation of Agentic AI-driven product capabilities within Protégé in PatentSight. This role focuses on defining what to build and why, leveraging machine learning … including hybrid search (semantic + lexical), and evaluation metrics (e.g., relevance, ranking quality). Analyse large-scale IP datasets to extract insights and improve model performance. Establish best practices for model evaluation, validation, and benchmarking. Translate experimental results into clear product recommendations and business impact. Collaborate with ...

regulated environments. Tomorrow’s enterprise security landscape will not look like today’s. As AI adoption accelerates, organisations face novel architectural challenges, ranging from model manipulation and prompt injection to sensitive data egress and supply chain vulnerabilities. Success requires embedding security directly into the AI development lifecycle, ensuring systems … Build & Lifecycle: Knowledge of how to integrate security controls into the AI build process (DevSecOps for AI/MLSecOps), including data pipeline security, model validation, and secure API integration. Data Protection & Egress Control: Deep awareness of data privacy challenges unique to AI, including data loss prevention (DLP), data ...

optimized for large-scale distributed systems using a high-performance grid computing platform Leverage Python, SQL, and Snowflake to analyze, construct, and validate model inputs Document methodologies to support internal and external model validation and compliance processes What you should have M.S. or PhD in mathematics, physical ...

teams deploy, monitor, retrain and govern models reliably at scale-from the ground up. Key Responsibilities ML Infrastructure & Deployment (40%): Build automated pipelines for model training, validation and deployment, plus model registries, feature stores and inference services, with self-serve tooling for Data Science teams. Model … workloads-covering latency, data quality and drift-and own rollback, rollout and incident response. MLOps Governance & Best Practice (20%): Establish controls for model lineage, reproducibility and audit trails, and introduce ML-specific CI/CD, testing and release automation. Collaboration & Enablement (10%): Partner with Data Science, Data Engineering ...

Ensure test coverage addresses CCR exposure calculations, netting, collateral and margining workflows, trade lifecycle events and relevant SA-CCR/IMM requirements. Oversee data validation and reconciliation testing across source feeds, risk engines and downstream outputs, investigating discrepancies with developers, business analysts and risk specialists. Maintain clear traceability between … timelines, requirements and proposed solutions where quality, testability or control standards may be compromised. Maintain testing documentation and evidence for internal audit, regulatory reviews, model validation teams and senior risk committees. Work with project managers, vendors, risk owners and technology leadership to resolve delivery issues and maintain consistent ...

decision-ready insights grounded in trusted data and established metrics.About the role:We are seeking a Data Scientist III to help the design and validation of Agentic AI-driven product capabilities within Protégé in PatentSight. This role focuses on defining what to build and why, leveraging machine learning … strategies, including hybrid search (semantic + lexical), and evaluation metrics (e.g., relevance, ranking quality).Analyse large-scale IP datasets to extract insights and improve model performance.Establish best practices for model evaluation, validation, and benchmarking.Translate experimental results into clear product recommendations and business impact.Collaborate with product, IP experts ...

enabled models, integrate diverse scientific data, and collaborate with multidisciplinary teams to drive discovery and decision-making. You will mentor junior colleagues, ensure robust model validation and governance, and contribute to a cutting-edge AI strategy across a global research network in an international environment. #J-18808-Ljbffr ...

years of longitudinal follow up and you will be expected to have or develop skills in longitudinal modelling, and development and validation of prediction models. The focus of this project is on biological ageing - with ageing clocks derived from retinal imaging, or proteomic analysis. We are interested ...

delivery within the Dreadnought Active Vehicle Control (AVC) Functional Software Team . You'll be part of a multi-disciplinary engineering environment adopting a Model Based Design approach-developing and validating behavioural/dynamic models used to derive requirements, support verification & validation activities, and gain stakeholder acceptance … Simulink Verify models meet user needs and maintain alignment with the system being modelled Integrate models into a wider whole-boat/vehicle control model framework Support deployment of models into real-time environments (e.g., rigs) Support model validation to ensure models are representative of real components ...

predictive and prescriptive modelling solutions.Data ValidationThis role is responsible for ensuring that the data being used is accurate, reliable, and suitable for the model being built. This includes tasks such as data cleaning, data verification, data transformation and validation methods using both statistical and machine learning methods.Model UsageThis … role assists with marrying business understanding and knowledge with existing, appropriate, predictive and prescriptive model offerings (machine learning and AI) for commercial and/or operational benefits, given a known business challenge.Model DeploymentThe Junior Data Scientist assists in the integration and implementation of relevant models into the applicable business ...

delivered on time, within budget and to a high quality. The role is accountable for driving the adoption of best practice across modelling, experimentation, validation and implementation, ensuring insights translate into practical and measurable business outcomes. The Data Science Manager will provide technical leadership across advanced analytics, machine learning … with strong SQL knowledge and experience using machine learning frameworks Experience with cloud platforms (Azure, AWS or GCP), data visualisation tools and MLOps/model deployment practices Understanding of data governance, responsible AI and Generative AI/LLM technologies; relevant professional qualification desirable At BDO, we’ll help ...

insights grounded in trusted data and established metrics.**About the role:** We are seeking a Senior Data Scientist I to help the design and validation of Agentic AI-driven product capabilities within Protégé in PatentSight. This role focuses on defining what to build and why, leveraging machine learning … including hybrid search (semantic + lexical), and evaluation metrics (e.g., relevance, ranking quality).* Analyse large-scale IP datasets to extract insights and improve model performance.* Establish best practices for model evaluation, validation, and benchmarking.* Translate experimental results into clear product recommendations and business impact.* Collaborate with ...

ready insights grounded in trusted data and established metrics. About the role: We are seeking a Data Scientist III to help the design and validation of Agentic AI-driven product capabilities within Protégé in PatentSight. This role focuses on defining what to build and why, leveraging machine learning … including hybrid search (semantic + lexical), and evaluation metrics (e.g., relevance, ranking quality). Analyse large-scale IP datasets to extract insights and improve model performance. Establish best practices for model evaluation, validation, and benchmarking. Translate experimental results into clear product recommendations and business impact. Collaborate with ...

ready insights grounded in trusted data and established metrics.**About the role:** We are seeking a Data Scientist III to help the design and validation of Agentic AI-driven product capabilities within Protégé in PatentSight. This role focuses on defining what to build and why, leveraging machine learning … including hybrid search (semantic + lexical), and evaluation metrics (e.g., relevance, ranking quality).* Analyse large-scale IP datasets to extract insights and improve model performance.* Establish best practices for model evaluation, validation, and benchmarking.* Translate experimental results into clear product recommendations and business impact.* Collaborate with ...

ready insights grounded in trusted data and established metrics. About the role: We are seeking a Data Scientist III to help the design and validation of Agentic AI-driven product capabilities within Protégé in PatentSight. This role focuses on defining what to build and why, leveraging machine learning … including hybrid search (semantic + lexical), and evaluation metrics (e.g., relevance, ranking quality). Analyse large-scale IP datasets to extract insights and improve model performance. Establish best practices for model evaluation, validation, and benchmarking. Translate experimental results into clear product recommendations and business impact. Collaborate with ...