51 to 75 of 164 Model Validation Jobs in the UK

and optimisation framework, conducting reviews of client's processes and procedures, support regulatory or audit reviews, advising on TM technology, optimisation, and remediation programmes Define and lead on TM model optimisation, industry monitoring typology risk assessment methodology and overall control framework in building an effective Transaction Monitoring programme Leading and developing strong relationships with project stakeholders Supporting with planning … defining change governance, project management and business analyst background. Must demonstrate strong capability to define and articulate approach Technical Skills: (Must have) Hands-on experience in defining and leading model validation and system tuning from inception to completion Hands-on understanding on minimum of one TM systems across rule designs, segment designs, technical design and processes (e.g NICE More ❯

paced, cross-functional teams to deliver data-driven solutions iterative * Familiarity with SQL and experience in working with relational databases. * Knowledge of data pre-processing techniques, feature engineering, and model evaluation metrics. This is an excellent opportunity on a great project of work, If you are looking for your next exciting opportunity, apply now for your CV to reach More ❯

engineering, target discovery, and experimental biology) to drive research projects that identify novel drug targets and preclinical candidates. Design and execute computational and experimental studies to validate and improve model predictions. Stay informed about the latest advancements in machine learning and computational biology, and apply them to real-world challenges. Share research findings through presentations, publications, and technical discussions. More ❯

the global/mandate processes are secured. Support Operations in complex and high-impact issues and problems as they arise. Accountable for process and technical knowledge gathering, update and validation of products and services under Reporting and Analytics tower, including documentation/knowledge from external teams that will touch those services and products. Support product managers on lifecycle management More ❯

the global/mandate processes are secured * Support Operations in complex and high impact issues and problems as they arise. * Accountable for process and technical knowledge gathering, update and validation of products and services under Reporting and Analytics tower, including documentation/knowledge from external teams that will touch those services and products * Support product managers on lifecycle management More ❯

the global/mandate processes are secured * Support Operations in complex and high impact issues and problems as they arise. * Accountable for process and technical knowledge gathering, update and validation of products and services under Reporting and Analytics tower, including documentation/knowledge from external teams that will touch those services and products * Support product managers on lifecycle management More ❯

ve got up your sleeve It would be great if you also have some of the following skills: Experience with Data Build Tool (DBT) including building data models, tests, validation, and transformations. In-depth knowledge of sports betting markets, including odds calculation, betting types and market trends Previous experience in the online gaming or casino industry, with a strong More ❯

creatively about how data can deliver value , A passion for building great data products and a willingness to roll up your sleeves across the data lifecycle - from pipelining to model deployment , Experience with some of the following tools and technologies (or an eagerness to learn): , Python and its data science ecosystem (e.g., pandas, scikit-learn, TensorFlow/PyTorch) , Statistical … methods and machine learning (e.g., A/B testing, model validation) , Data pipelining tools like SQL, dbt, BigQuery, or Spark , A strong communicator with the ability to communicate technical concepts into layman's terms for a non-technical audience , You're not afraid to challenge the status quo if it means reaching a better outcome for our customers More ❯

creatively about how data can deliver value. A passion for building great data products and a willingness to roll up your sleeves across the data lifecycle - from pipelining to model deployment. Experience with some of the following tools and technologies (or an eagerness to learn): Python and its data science ecosystem (e.g., pandas, scikit-learn, TensorFlow/PyTorch). … Statistical methods and machine learning (e.g., A/B testing, model validation). Data pipelining tools like SQL, dbt, BigQuery, or Spark. A strong communicator with the ability to communicate technical concepts into layman's terms for a non-technical audience. You're not afraid to challenge the status quo if it means reaching a better outcome for More ❯

as a Simulation & Modelling Engineer with a world class technical engineering company based in Bristol. The opportunity: The Principal Simulation & Modelling Engineer will focus on the development, integration, and validation of dynamic numerical models, primarily using MATLAB and Simulink. Working with all elements of a system right down to wider system component level, the Principal Simulation & Modelling Engineer will … lifecycle. Automation is a key part of our testing process, and we are hands on in putting continuous integration and verification processes in place, in conjunction with performing detailed model validation against experimental or field trial data. Ensuring quality standards are upheld is also important in our work, so all Simulation & Modelling Engineers participate in this through model … Simulink, or other programming languagesfor system modelling and simulation. Knowledge of coding standards and peer review of coding changes Strong understanding of dynamic systems and numerical modelling. Familiarity with model-based design methodologies, the software development cycle, and systems engineering processes. Knowledge of scripting and automation (MATLAB scripts, Gitlab, Jenkins, or similar). Excellent problem-solving, analytical, and communication More ❯

evaluation, and monitoring of models (LLMs, predictive/classification, agentic workflows), ensuring security and compliance standards are met. Implement best practices for MLOps, including CI/CD, automated testing, model versioning, and data validation. Architect integrations with vector databases, cloud platforms, and retrieval-augmented generation systems. Qualifications: 3+ years of experience delivering full-stack AI/ML applications in More ❯

models in areas such as Automated Valuation Models (AVMs) and propensity modelling. You will take full ownership of the end-to-end data science lifecycle-from problem scoping to model deployment-while also setting technical standards, shaping strategy, and managing a team of three skilled data scientists. Key Responsibilities Manage a team of 3 data scientists, fostering a high … Set the vision and strategy for data science within the business, championing best practices and innovation Communicate complex technical concepts to both technical and non-technical stakeholders Guide experimentation, model validation, and continuous performance monitoring Contribute to long-term planning for the company's data science roadmap Tech Environment Strong emphasis on Python and cloud-based data science … tooling Experience with model deployment in production is essential Understanding of real estate or consumer behaviour data is a bonus, but not required About You Proven experience leading small data science teams in a commercial setting Strong expertise in machine learning, particularly with propensity modelling/AVM's Deep experience with end-to-end model development and deployment More ❯

Security Manage the security for the firms externally facing AI products, including ContractMatrix and other AI products currently in development by the firm. Establish and embed processes for secure model development, training, and deployment of AI products. Ensure that AI model behaviour in the firms AI products is continuously monitored for any anomalies and/or potential security … to understand complex AI models in order to give tailored security advice. Strong understanding of AI related data protection laws, and ethical frameworks. Familiarity with AI risk management tools, model validation, and regulatory reporting requirements. Excellent communication and stakeholder engagement skills, with the ability to bridge technical, and business perspectives. Confident in discussing complex AI models with product … or legal sector organisations. Certifications in AI ethics, data privacy (e.g., CIPP/E, CIPM), or risk management (e.g., CRISC). Experience with AI auditing, algorithmic impact assessments, or model governance platforms. Knowledge of legal technology tools and platforms (e.g., legal research AI, contract analytics, generative AI). Ability to lead cross-functional initiatives in a complex, multinational environment. More ❯

Manage the security for the firm's externally facing AI products, including ContractMatrix and other AI products currently in development by the firm. Establish and embed processes for secure model development, training, and deployment of AI products. Ensure that AI model behaviour in the firm's AI products is continuously monitored for any anomalies and/or potential … to understand complex AI models in order to give tailored security advice. Strong understanding of AI related data protection laws, and ethical frameworks. Familiarity with AI risk management tools, model validation, and regulatory reporting requirements. Excellent communication and stakeholder engagement skills, with the ability to bridge technical, and business perspectives. Confident in discussing complex AI models with product … or legal sector organisations. Certifications in AI ethics, data privacy (e.g., CIPP/E, CIPM), or risk management (e.g., CRISC). Experience with AI auditing, algorithmic impact assessments, or model governance platforms. Knowledge of legal technology tools and platforms (e.g., legal research AI, contract analytics, generative AI). Ability to lead cross-functional initiatives in a complex, multinational environment. More ❯

and optimisation framework, conducting reviews of client's processes and procedures, support regulatory or audit reviews, advising on TM technology, optimisation, and remediation programmes Define and lead on TM model optimisation, industry monitoring typology risk assessment methodology and overall control framework in building an effective Transaction Monitoring programme Leading and developing strong relationships with project stakeholders Supporting with planning … change governance, project management and business analyst background. Must demonstrate strong capability to define and articulate approach Technical skills (50%) (Must have) Hands-on experience in defining and leading model validation and system tuning from inception to completion Hands-on understanding on minimum of one TM systems, rule designs, segment designs, technical design and processes (e.g NICE Actimize More ❯

as a Simulation & Modelling Engineer with a world class technical engineering company based in Bristol. The opportunity: The Principal Simulation & Modelling Engineer will focus on the development, integration, and validation of dynamic numerical models, primarily using MATLAB and Simulink. Working with all elements of a system right down to wider system component level, the Principal Simulation & Modelling Engineer will … lifecycle. Automation is a key part of our testing process, and we are hands on in putting continuous integration and verification processes in place, in conjunction with performing detailed model validation against experimental or field trial data. Ensuring quality standards are upheld is also important in our work, so all Simulation & Modelling Engineers participate in this through model … or other programming languages for system modelling and simulation. Knowledge of coding standards and peer review of coding changes Strong understanding of dynamic systems and numerical modelling. Familiarity with model-based design methodologies, the software development cycle, and systems engineering processes. Excellent problem-solving, analytical, and communication skills. Experience with version control tools (e.g., Git, EWM). Hands-on 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 ❯

Define benchmarks for target selection and biomarker tasks; source or assemble reference datasets to measure progress. Apply causal inference principles to propose experiments that disambiguate biological mechanisms and validate model outputs. Write high quality, tested code and deploy models across multi GPU or HPC environments; monitor performance and iterate. Stay current with advances in machine learning and computational biology More ❯

on will be diverse and will demand a strong proficiency in multiple technologies, primarily Python and SQL. Your tasks may include feature engineering for machine learning models or data validation, integrating with external APIs, developing internal tools, creating REST APIs, and managing databases. The requirements Essential: Python software engineering experience. SQL experience. Happy working in an unstructured, dynamic, and More ❯

Strategy: Identify, engage, and manage stakeholders to ensure alignment and buy-in. Investigation & Requirements Engineering: Use interviews, workshops, and observation to elicit and document high-quality requirements. Requirements Modelling & Validation : Apply modelling techniques (e.g., BPMN, UML) and validate outputs with stakeholders. Business Process & Data Modelling: Map current and future state processes and data flows to support solution design. Idea More ❯

Python data science space - LangChain, LangSmith, pandas, numpy, sci kit learn, scipy, hugging face etc. Understanding of statistical and machine learning models. Knowledge of experimental design, statistical testing and model validation. Experience in data visualization tools such as plotly, seaborn, streamlit etc would be an advantage. Understanding of data modelling and exposure to tools like metaflow or airflow is More ❯

as a governance and architecture lead across multi-party AI initiatives. 4. Risk, Compliance & Responsible AI Ensure solutions are aligned with regulatory and compliance requirements (e.g., FINRA, SEC, GDPR, Model Risk Governance). Design solutions with embedded safeguards around transparency, explainability, auditability, and ethical AI usage. 5. Knowledge Development & Enablement Create reusable playbooks, frameworks, and GenAI solution kits specific … in both business and technology. Experience working in or with regulated financial environments and navigating security and compliance requirements. Preferred Qualifications Experience with retrieval augmented generation (RAG), fine-tuning, model evaluation, or deploying models in production environments. Understanding of financial risk modeling, model validation, or operational risk frameworks. Knowledge of regulatory frameworks such as DORA, SR More ❯

as a governance and architecture lead across multi-party AI initiatives. 4. Risk, Compliance & Responsible AI Ensure solutions are aligned with regulatory and compliance requirements (e.g., FINRA, SEC, GDPR, Model Risk Governance). Design solutions with embedded safeguards around transparency, explainability, auditability, and ethical AI usage. 5. Knowledge Development & Enablement Create reusable playbooks, frameworks, and GenAI solution kits specific … in both business and technology. Experience working in or with regulated financial environments and navigating security and compliance requirements. Preferred Qualifications Experience with retrieval augmented generation (RAG), fine-tuning, model evaluation, or deploying models in production environments. Understanding of financial risk modeling, model validation, or operational risk frameworks. Knowledge of regulatory frameworks such as DORA, SR More ❯

financial decisions across global markets. Responsibilities: Lead the development and maintenance of credit risk and affordability models using bureau, open banking, and alternative behavioural data. Own end-to-end model lifecycle: data sourcing, feature engineering, model development, validation, and monitoring. Design and execute champion/challenger tests and A/B experiments to continuously improve approval rates … of analysts/data scientists as the team scales Work closely with Data Engineering to deploy models into production pipelines. Collaborate with stakeholders to define modelling goals and interpret model outcomes in a business context. Requirements: MSc or PhD Degree in Computer Science, Artificial Intelligence, Mathematics, Statistics or related fields. Strong coding skills in Python and SQL Strong communication More ❯

financial decisions across global markets. Responsibilities: Lead the development and maintenance of credit risk and affordability models using bureau, open banking, and alternative behavioural data. Own end-to-end model lifecycle: data sourcing, feature engineering, model development, validation, and monitoring. Design and execute champion/challenger tests and A/B experiments to continuously improve approval rates … of analysts/data scientists as the team scales Work closely with Data Engineering to deploy models into production pipelines. Collaborate with stakeholders to define modelling goals and interpret model outcomes in a business context. Requirements: MSc or PhD Degree in Computer Science, Artificial Intelligence, Mathematics, Statistics or related fields. Strong coding skills in Python and SQL Strong communication More ❯