51 to 75 of 328 Model Validation Jobs in England

senior scientific, technical, quality and enterprise leaders.Strong track record managing budgets, headcount and resources, including external technology partners and contractors.Working knowledge of data governance, model validation, data quality and responsible AI practice in a regulated Good Practice (GxP) environment.Strong analytical skills. You define portfolio-level metrics … culture of technical excellence and continuous improvement.Work Location and Flexibility This role is based in the United States and offers a hybrid work model, combining remote work with on-site collaboration to foster strong teamwork and engagement. #LI-GSK#AGTechSkillsArtificial Intelligence (AI), Artificial Intelligence Ethics, Artificial Neural Networks (ANNS), Classification ...

post holder will work across the full application lifecycle, including system configuration, software deployment, Linux server administration, scripting and automation, troubleshooting, GIS–ADMS model integration, data quality, data mastery, system integration, testing, and change management activities. The role will help the delivery of new functionality, upgrades, interfaces, and operational … integration of GIS network models into ADMS platforms to support LV Management use cases. Implement and maintain GIS adaptor technologies to ensure accurate model exchange between GIS and ADMS. Help end‐to‐end delivery from data extraction and transformation through to ADMS model validation and operational acceptance. ...

Monzo.This role combines near-term commercial impact with long-term strategic advantage:- improving performance across our range of products- unlocking new data and faster model development- helping Monzo build underwriting that scales into new markets- shaping how advanced ML and AI can create real value in BorrowingYou’ll partner … closely with leaders across Credit Strategy, Product, Engineering, Model Validation, Data, and Platform, and influence decisions that shape both business performance and the long-term architecture of credit decisioning at Monzo.What you’ll own- Borrowing ML roadmaps across UK optimisation, new customer segments, and EU expansion- Faster model ...

technical hardware-and-software architect role, not a strategy-only position. The successful candidate operates comfortably across GPU infrastructure, high-performance networking, model training and inference pipelines, and the AI risk/governance disciplines increasingly demanded by regulators and enterprise boards. The Enterprise Architect will lead technical delivery teams … inference/serving platforms, MLOps/LLMOps tooling, vector databases, and retrieval-augmented generation (RAG) and agentic architectures. Define and AI governance frameworks covering model risk management, responsible AI, data lineage, bias/fairness testing, explainability, and regulatory alignment (EU AI Act, NIST AI RMF, ISO/ ...

immunohistochemistry, spatial transcriptomics and clinical variables. Build and maintain reproducible pipelines for slide preprocessing, stain normalisation, tissue and cell segmentation, feature extraction and model evaluation, using version control and clear documentation. Curate and manage large imaging and clinical datasets in line with ethics approvals, data protection … information governance requirements. Carry out external validation across centres and scanners, and assess model calibration, bias and failure modes. Bring histopathological and computational thinking together, working with pathologists to design annotation protocols, check that model outputs make morphological sense and interpret explainability analyses. Support the principal investigator ...

business or user problems. Work with data engineers, analysts, developers and product teams to deliver usable data science solutions. Document methods, assumptions, limitations and validation results clearly and responsibly. Consider data ethics, privacy and security throughout the data science life cycle. Essential skills and experience Experience applying data science … methods to real-world problems. Good knowledge of Python, R or similar programming languages for analysis and modelling. Understanding of machine learning, statistical testing, model validation and performance metrics. Ability to prepare, clean and transform data for analysis and modelling. Ability to communicate technical findings and visualisations ...

development ensuring quality reliability performance security and complianceDesign and execute test plans for LLM powered applications chatbots copilots RAG solutions and AI agentsValidate AI model accuracy bias fairness explainability robustness and performanceDevelop and maintain AI test automation frameworks using Python and modern testing toolsPerform functional integration regression API performance … security testing for AI platformsEstablish test data management practices and synthetic data generation approachesDefine and monitor AI quality metrics model drift hallucination rates response consistency and user experience KPIsCollaborate with Data Scientists AI Engineers Product Owners Architects and business stakeholdersSupport CICD integration and quality gates for AI model ...

quality reliability performance security and compliance Design and execute test plans for LLM powered applications chatbots copilots RAG solutions and AI agents Validate AI model accuracy bias fairness explainability robustness and performance Develop and maintain AI test automation frameworks using Python and modern testing tools Perform functional integration regression … performance and security testing for AI platforms Establish test data management practices and synthetic data generation approaches Define and monitor AI quality metrics model drift hallucination rates response consistency and user experience KPIs Collaborate with Data Scientists AI Engineers Product Owners Architects and business stakeholders Support CICD integration ...

deep-learning framework (TensorFlow/PyTorch). You possess a solid grasp of core data science techniques (supervised/unsupervised learning, time-series, NLP, model validation) and the ability to innovate new algorithms. You apply a rigorous scientific and entrepreneurial mindset, translating complex business problems into a mathematical … framework and measuring model impact upon deployment. You are an exceptional communicator, adept at translating complex technical solutions into persuasive, actionable insights for senior and non-technical audiences. You contribute to team success by project planning, assessing technical feasibility, estimating delivery timelines, and achieving measurable outcomes. Our Interview Process ...

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 ...

Monzo.This role combines near-term commercial impact with long-term strategic advantage :improving performance across our range of productsunlocking new data and faster model developmenthelping Monzo build underwriting that scales into new marketsshaping how advanced ML and AI can create real value in BorrowingYou’ll partner closely with leaders … across Credit Strategy, Product, Engineering, Model Validation, Data, and Platform, and influence decisions that shape both business performance and the long-term architecture of credit decisioning at Monzo.What you’ll ownBorrowing ML roadmaps across UK optimisation, new customer segments, and EU expansionFaster model improvement through new data ...

with clarity, speed, and confidence. If you are excited about this opportunity but do not meet every single requirement, we are seeking candidates who model our values: invest in every relationship, lead with curiosity, champion diverse perspectives, turn inputs into actions, and uphold trust through integrity. Skills and Competencies … Strong foundation in hydrology, hydraulics, climate science, applied mathematics, data science, computer science, or a related quantitative discipline Understanding of machine learning fundamentals including model training and validation, overfitting, and model evaluation Experience with one or more machine learning or deep learning approaches such as Long Short ...

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 ...

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 ...