76 to 100 of 328 Model Validation Jobs in England

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 ...

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 ...

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 ...

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 ...

Science 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 Monitoring & Operations … recovery for ML 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 ...

degree in a technical field and Python experience are required, with familiarity in AI/ML concepts and Agile methods. You will contribute to model validation, testing, and delivering small to medium AI features in collaboration with MLOps and #J-18808-Ljbffr ...

production, working with engineering to deploy on GCP. Investigate new data sources and methods to close gaps in maritime domain awareness. Communicate findings and model behavior clearly to both technical and non-technical stakeholders, including customers. Collaborate closely with the wider Theia data science and engineering teams on shared ...

Prepare reliable infrastructure for future commercial deployment. Engineering Data, Simulation & Computational Modelling • Develop simulation, optimisation, computational modelling and Digital Twin tools for autonomous machinery. • Model machine performance, operating sequences, capacity and efficiency. • Analyse machine, sensor and operational data and test engineering concepts before physical implementation. • Work with robotics ...

language for the large-scale analysis and production of securities data Research and development of market dynamics across multiple asset classes Prototyping, testing, and validation of our client's proprietary models supporting and coaching more junior team members Requirements Proven financial markets experience, ideally within an investment bank ...

that private equity-backed businesses use to run their value creation plans. As Vice President, Data Solutions, you’ll own the architecture, data operating model and client relationship. You’ll work out what the business needs and why, shape the design, define how data is owned, governed and managed ...

data engineers, technical project managers, marketing operations, and account leadership. Advanced expertise in data architecture for campaign execution, including complex data models, audience selection, validation, identity resolution, QA, and data-driven trigger logic. Relevant platform certifications or accreditations are strongly preferred (e.g., Adobe Certified Master/Expert ...

across platforms such as Relativity, Epiq AI, Reveal AI, and other advanced review or analytics platforms.* Develop and refine prompts, instructions, classification criteria, and model guidance for generative AI and LLM-enabled tools, with a focus on consistency, quality, defensibility, and practical review outcomes.* Advise clients and internal teams … appropriate use cases, limitations, risks, defensibility considerations, and quality control measures for AI-enabled review workflows.* Explain statistical concepts, validation methodologies, sampling approaches, model performance, and review metrics to audiences with varying levels of technical, legal, and operational knowledge.* Demonstrate a strong understanding of the full eDiscovery lifecycle ...

cross-asset workflows. What's the role? We are looking for a senior product manager to run IPQA's analytics architecture and drive model delivery at scale, ensuring our analytics continue to lead as client and market needs evolve. You will work at the intersection of quantitative finance, engineering … clients across Terminal, Enterprise Data and Enterprise Products. At the core of this role is a founding conviction: analytics at scale require a model-agnostic design, where the architecture enforces consistency and transparency across models, asset classes and client workflows. You will work closely with our quant and engineering ...

platforms Develop and apply data quality frameworks to assess and ensure fitness-for-purpose of diverse data sources for specific analytical questions Implement strong model evaluation practices including cross-validation strategies, calibration assessment, and transparent reporting of model performance and limitations Build scalable, automated processes for delivering … health data and wearable/sensor-derived data types Experience with cloud-based scalable compute and deployment patterns for large-scale data processing and model training #LI-Hybrid We hire for skills and capabilities, not just credentials. How We Work Where you work matters – because collaboration, innovation and patient ...

Support clients in navigating emerging AI regulations, standards, and guidance while enabling innovation and business value. Facilitate workshops, governance forums, risk assessments, and operating model design activities with senior stakeholders. Live in-person whiteboarding sessions to problem solve as a team, alongside asynchronous communication on Teams. Hybrid working with … client site. You must be an established consulting professional with experience delivering projects focused on AI governance, responsible AI, AI risk management, model governance, or emerging technology regulation within the private sector. Ideally, you should have experience consulting with Life Sciences, Government or Consumer & Manufacturing sector clients. Even ...

commercial curiosity. The Data Scientist will own selected use cases across the full lifecycle—from problem definition, data exploration and feature engineering through to model development, deployment, monitoring and continuous improvement and will be accountable for demonstrating measurable operational or commercial value. The role will also influence technical … support strategic and operational decision making. Develop, validate and deploy predictive models, forecasting models and AI solutions that deliver measurable business value. Evaluate model effectiveness and monitoring model drift. Develop and maintain governed, trusted datasets for BI/analytics and data science use cases. Ensure appropriate data quality ...

commercial curiosity. The Data Scientist will own selected use cases across the full lifecycle—from problem definition, data exploration and feature engineering through to model development, deployment, monitoring and continuous improvement—and will be accountable for demonstrating measurable operational or commercial value. The role will also influence technical … support strategic and operational decision making. Develop, validate and deploy predictive models, forecasting models and AI solutions that deliver measurable business value. Evaluate model effectiveness and monitor model drift. Develop and maintain governed, trusted datasets for BI/analytics and data science use cases. Ensure appropriate data quality ...

exploration to deployment, turning insights into production-ready models for international clients. You will collaborate with diverse teams, handle data cleaning, feature engineering, and model validation, and contribute to cross-functional projects across US and Europe. #J-18808-Ljbffr ...

anomaly detection, variance analysis, drift detection, forecasting, behavioural signals, and prediction, ensuring they are accurate, explainable, resilient, and regulator-ready. Own the full model lifecycle from problem framing, data exploration, feature engineering, back-testing, validation, deployment, monitoring, tuning, and incident response when models degrade or fail in production. … models within financial services or other highly regulated, high‐availability environments. Deep expertise in applied data science and ML, including statistical modelling, predictive analytics, model development, validation, deployment, monitoring, and optimisation across the full model lifecycle. Strong hands‐on technical skills, with the ability to work directly ...

customer service, labour productivity, costs and budgets. Utilise data from various sources (Fabric, WMS, SAP ect.) to spot trends, risks and root causes. Validate, model and manage the data behind reports. Automating manual reporting and finding ways to make information easier to access. Working with stakeholders across the business ...