1 to 25 of 335 Model Validation Jobs in London

time-series analysis, machine learning, and financial theory to develop accurate, commericalizable financial models. Collaborate closely with engineering, data science, product management, and commercial teams to maintain and enhance model features, data pipelines, and analytical tools. Ensure model validation, back-testing, and continuous performance assessment, prioritizing scalability, reliability, and integrity. Provide thought leadership and strategic guidance on More ❯

formulation of input and output information from the global energy markets. The position is based in London, Houston, Singapore, or Bangalore. The analyst bridges seven areas: Assessment that a model is “fit for purpose” Analysis of model algorithms applying validation techniques, including challenger models internally built or externally procured Review of the process underlying a model, including inputs and outputs flows Determination of adjustments to models to mitigate weaknesses Maintenance and extension of the Model Validation policy Inventory of models used within the Risk Management Function (RMF) and externally as requested Collection of code and documentation for successive model versions The activities involve professionals at three levels: The Senior Analyst investigates conceptual … underpinnings and audits embedded algorithms for limitations and remediations. The Engineer considers application cases, formulates test scripts, and verifies input and output appropriateness. The Junior Analyst compiles model information and executes test scripts. Engagement with external stakeholders is significant, involving groups within RMF such as Quantitative Analysis, Portfolio Valuation, Market Risk Management, and others, as well as interface with More ❯

Join to apply for the Model Validation - Specialist role at Shell Join to apply for the Model Validation - Specialist role at Shell What You’ll Be Doing The candidate will participate in the assignments of the Group validating Models used in the for valuation and risk measurement of Financial Portfolios, Assets and Instruments as well as … of Input and Output information from the Global Energy markets. She or he is based in London, Houston, Singapore or Bangalore. The analyst bridges seven areas: Assessment that a Model is “fit for purpose” Analysis of Model algorithms applying different validation techniques which include using challenger models internally built or procured externally Review of process underlying a … Model including inputs and outputs flows Determination of adjustments to Model required to mitigate weaknesses Maintenance and extension of the Model Validation policy Inventory of Models used within the Risk Management Function (RMF) and without as requested Collection of code and documentation for successive Model versions The different professionals involved in these activities along with More ❯

to implement an effective risk governance framework across MUFG EMEA, and providing a holistic view of the risks facing MUFG in EMEA, including environmental and social risk management. The Model Risk Management (MRM) within ERM is responsible for model governance and the validation of models used by MUFG in EMEA. This includes, among others, risk models which … with Risk Analytics and Front Office quants to ensure that all risk models are validated on a periodic basis as well as at inception and changes. MRM provides regular model risk reporting to model oversight committees and the Board. MAIN PURPOSE OF THE ROLE Independent model validation of quantitative methodologies, both initial and periodic, across all … asset classes and model types (derivative pricing models, credit and market risk, capital models, AI models, etc. ) and in line with regulatory requirements and industry best practice. The validation regularly requires an independent implementation of the models and the implementation of alternative challenger models. KEY RESPONSIBILITIES Initial and periodic validation of quant models Designing, modelling and prototyping More ❯

often not fit-for-purpose. The ideal candidate has to have the ability to customis e source cod e , add in new features and code from scratch . Whilst model deployment/software development experience is highly desirable, we do have a team of engineers to support so exposure in this space will be sufficient . Culture-wise, we … ACO algorithm(s) and related Data Science compone n ts for the product Design and contribute to the end-to-end machine learning pipeline from data collection, reprocessing to model training, simulation, evaluation, deployment and experimentation/testing Implement and interpret explainability frameworks to provide clear insights into model decisions, ensuring transparency and compliance with WPP standards Collaborate … with stakeholders to identify business needs and translate these requirements into technical solutions that are scalable and impactful Conduct rigorous model testing and validation to ensure robustness and accuracy Prepare detailed documentation and reports that communicate complex model behaviours, predictions, and insights in a manner accessible to both technical and non-technical audiences Stay abreast of academic More ❯

the candidate needs to be truly, technically competent, to be able to quickly learn a complex system, customise source code, add in new features and code from scratch. Whilst model deployment/software development experience is very, highly desirable, we do have a team of engineers to support so exposure in this space will be sufficient. Culture-wise, we … Enterprise. Key Responsibilities: Further improve existing algorithms and develop net new DS features Design and contribute to the end-to-end machine learning pipeline from data collection, reprocessing to model training, simulation, evaluation, deployment and experimentation/testing Implement and interpret explainability frameworks to provide clear insights into model decisions, ensuring transparency and compliance with WPP standards Collaborate … with stakeholders to identify business needs and translate these requirements into technical solutions that are scalable and impactful Conduct rigorous model testing and validation to ensure robustness and accuracy Prepare detailed documentation and reports that communicate complex model behaviours, predictions, and insights in a manner accessible to both technical and non-technical audiences Stay abreast of academic More ❯

train, and deploy state-of-the-art models (e.g., deep learning, NLP, computer vision, reinforcement learning, or relevant domain-specific architectures). Design infrastructure for data ingestion, annotation, experimentation, model versioning, and monitoring. Collaborate closely with product, design, and DevOps to integrate AI features into our platform. Continuously evaluate new research, open-source tools, and emerging frameworks to keep … train, and deploy state-of-the-art models (e.g., deep learning, NLP, computer vision, reinforcement learning, or relevant domain-specific architectures). Design infrastructure for data ingestion, annotation, experimentation, model versioning, and monitoring. Collaborate closely with product, design, and DevOps to integrate AI features into our platform. Continuously evaluate new research, open-source tools, and emerging frameworks to keep … ML team as we scale beyond our seed round. ⸻ Key Responsibilities Architecture & Hands-On Development Define and implement end-to-end AI pipelines: data collection/cleaning, feature engineering, model training, validation, and inference. Rapidly prototype novel models (e.g., neural networks, probabilistic models) using PyTorch, TensorFlow, JAX, or equivalent. Productionize models in cloud/on-prem environments (AWS More ❯

solve business problems but do so optimally. Additionally, you're not just building and validating models – you’re deploying them as code to applications and processes, ensuring that the model(s) you've selected sustains its business value throughout its lifecycle. Your expertise doesn't stop at data; you'll become intimately familiar with our business processes and have More ❯

job is with Santander UK, an inclusive employer and a member of myGwork – the largest global platform for the + business community. Please do not contact the recruiter directly. Model Validation Quantitative Analytics Manager | S3 | Model Risk Country: United Kingdom Interested in part-time, job-share or flexible working? We want to talk to you! Join our … community. Model Risk is a vital aspect of managing risk that affects all areas of the bank, involving two model validation teams (Non-Retail and Retail) and a Model Risk control team. In this challenging role within the Non-Retail Internal Validation Team, you'll contribute to the model validation supporting business and … ve performed a similar role previously, this could be the perfect opportunity to develop your career at the heart of the action. The difference you'll make: Being a Model Risk subject matter expert for key stakeholders, both within and outside of Risk department Contributing to the planning for key projects to ensure the most effective use of validation More ❯

with 36 offices worldwide, the Group has over 1,800 employees across Europe, Asia and America. For more information visit www.marex.com The Quantitative Analyst will continuously be challenged around model risk management, model validation, pricing methodology and quantitative model development of various pricing and risk engines. They will gain exposure to various asset classes with a … strong appreciation for the complexities across the various commodity and equity markets. Development of independent coding libraries and routines is required. Responsibilities: Contribute to the Model Risk Management framework for Structured Financial products and exotic trades. Contribute to independent model validation of Front Office Analytics libraries and models for equities, FX, Credit and commodities. Produce high quality … quantitative analysis and model validation documentation (LaTeX). Enhance the risk management infrastructure through the transformation of data with coding. Ongoing model development for valuation and risk measurement, carrying out reviews and calibration of model parameters to help ensure best practice is followed. Develop and implement tactical & strategic risk tools to provide analysis and potential reporting More ❯

candidate will have a background in optimization, strong statistical skills, and experience with cloud technologies. You must be capable of quickly understanding and customizing complex in-house systems, with model deployment or software development experience being highly desirable. We value team players passionate about applying data science to solve complex problems and drive innovation. Key Responsibilities: Enhance existing algorithms … Design and contribute to end-to-end machine learning pipelines Implement explainability frameworks to ensure transparency and compliance Collaborate with stakeholders to translate business needs into technical solutions Conduct model testing and validation for robustness and accuracy Document and communicate complex model behaviors and insights Stay updated on academic and industry advancements in optimization and AI/… algorithms or related ML disciplines, preferably with practical, real-world data experience Proficiency in Python (and ML frameworks) and SQL Experience with cloud technologies (AWS or GCP) Exposure to model deployment and software development Strong statistical and machine learning knowledge Effective communication skills and a collaborative attitude Highly desirable qualifications: Experience with Airflow Commercial software development experience ML Ops More ❯

statistical knowledge, and hands-on cloud technology experience. You should be truly, technically competent to learn complex systems quickly, customize source code, add new features, and code from scratch. Model deployment and software development experience are highly desirable, but support from our engineering team will be available. We value team players passionate about applying Data Science to solve complex … build scalable, global products. Key Responsibilities: Further improve existing algorithms and develop new DS features Design and contribute to end-to-end machine learning pipelines, from data collection to model deployment and testing Implement explainability frameworks for model transparency and compliance Collaborate with stakeholders to translate business needs into technical solutions Conduct rigorous model testing and validation … algorithms or related ML disciplines (practical, real-world data problem-solving preferred) Proficiency in Python (and ML frameworks) and SQL Experience with cloud technologies (AWS or GCP) Exposure to model deployment and software development Strong statistical and machine learning knowledge Effective communication skills for collaboration across technical and non-technical teams Highly Desirable Qualifications: Hands-on experience with Airflow More ❯

analysis to assess and predict financial risks including input into regulatory processes/reporting. Provide regular reports and insights to senior leadership, highlighting emerging risks and their potential impact. Model Risk Management (including statistical, advanced AI/ML based techniques) - Formulation of guidelines/policy, Laying down of Roadmap, Establishment of model risk governance including frameworks, validation … GAAP) and regulatory requirements (e.g., Basel III, IFRS 9). Statistical Techniques: Linear & Logistic Regression, Hypothesis Testing, Exploratory Data Analysis, Survival Analysis, Cluster Analysis, various Statistical Tests and Cross-Validation Techniques, ML Algorithms. Proficiency in programming languages like Python, R, or MATLAB for quantitative modeling. Soft Skills: Strong analytical and problem-solving skills with attention to detail. Ability to More ❯

of customizing source code, adding new features, and coding from scratch, as off-the-shelf solutions often are not fit-for-purpose at this scale and complexity. Exposure to model deployment and software development will be sufficient, supported by our engineering team. We seek a great team player passionate about applying Data Science techniques to solve complex problems and … the ACO algorithm(s) and related Data Science components for the product. Design and contribute to the end-to-end machine learning pipeline, from data collection and reprocessing to model training, simulation, evaluation, deployment, and testing. Implement and interpret explainability frameworks to ensure transparency and compliance with WPP standards. Collaborate with stakeholders to identify business needs and translate these … into scalable, impactful technical solutions. Conduct rigorous model testing and validation for robustness and accuracy. Prepare detailed documentation and reports to communicate complex model behaviors, predictions, and insights to both technical and non-technical audiences. Stay updated on academic research and industry advancements in RL and AI/ML. Share knowledge and support the wider team and More ❯

What You’ll Be Doing: Designing and building machine learning models to solve real-world problems Carrying out full data science workflows: from data acquisition and cleaning to modelling, validation, and deployment Applying statistical and AI techniques to generate actionable insights Contributing to experimental research, model prototyping, and A/B testing Presenting findings clearly to both technical … a related discipline Strong programming skills in Python (e.g. NumPy, pandas, scikit-learn, matplotlib); R is also welcome A solid understanding of core machine learning concepts, data wrangling, and model evaluation Proficiency with SQL and experience handling large datasets A passion for solving complex problems using data and a continuous learning mindset Excellent communication and collaboration skills Full right More ❯

earthquakes. Comprised of enthusiastic and team-oriented individuals, the UK Property team continually evolves the way clients understand and manage catastrophe risk. Close collaboration with brokers, academic partners, and model evaluation specialists enables the team to leverage expert insights and robust analysis to help clients make more informed decisions. Key Responsibilities Act as the client analytics lead on reinsurance … MSc or higher qualification ideally in finance, statistics, science, or a related field. Proficient in the following areas: Catastrophe modelling with vendor tools (e.g. RMS, AIR, JBA, CoreLogic) Catastrophe model validation and evaluation Implementation of climate change solutions Strong communication skills across speaking, listening, and writing Proficiency in Microsoft Office suite; programming/machine learning experience a plus More ❯

What You’ll Be Doing: Designing and building machine learning models to solve real-world problems Carrying out full data science workflows: from data acquisition and cleaning to modelling, validation, and deployment Applying statistical and AI techniques to generate actionable insights Contributing to experimental research, model prototyping, and A/B testing Presenting findings clearly to both technical … a related discipline Strong programming skills in Python (e.g. NumPy, pandas, scikit-learn, matplotlib); R is also welcome A solid understanding of core machine learning concepts, data wrangling, and model evaluation Proficiency with SQL and experience handling large datasets A passion for solving complex problems using data and a continuous learning mindset Excellent communication and collaboration skills Full right More ❯

with generative AI, machine learning, or other algorithmic solutions Process structured and unstructured data for generative AI use cases Collaborate with engineering and product development teams Following experimentation and model selection and validation, implement the solution using production-ready Python code Contribute to the full software development lifecycle, including requirements gathering, design, development, testing, deployment, and maintenance of … machine learning solutions Monitor model performance post-deployment and iterate based on feedback Communicate with, and present results to, colleagues and stakeholders Present information using data visualization techniques Qualifications/Requirements: A master’s degree (or equivalent) in a numerate discipline such as Statistics, Machine Learning, Computer Science, Engineering, or Physics. A doctorate is a plus but not required More ❯

shelf solutions are often not fit-for-purpose. The ideal candidate has to have the ability to customise source code, add in new features and code from scratch. Whilst model deployment/software development experience is highly desirable, we do have a team of engineers to support so exposure in this space will be sufficient. Culture-wise, we’re … optimize the ACO algorithm(s) and related Data Science components for the product Design and contribute to the end-to-end machine learning pipeline from data collection, reprocessing to model training, simulation, evaluation, deployment and experimentation/testing Implement and interpret explainability frameworks to provide clear insights into model decisions, ensuring transparency and compliance with WPP standards Collaborate … with stakeholders to identify business needs and translate these requirements into technical solutions that are scalable and impactful Conduct rigorous model testing and validation to ensure robustness and accuracy Prepare detailed documentation and reports that communicate complex model behaviours, predictions, and insights in a manner accessible to both technical and non-technical audiences Stay abreast of academic More ❯

with generative AI, machine learning, or other algorithmic solutions Process structured and unstructured data for generative AI use cases Collaborate with engineering and product development teams Following experimentation and model selection and validation, implement the solution using production-ready Python code Contribute to the full software development lifecycle, including requirements gathering, design, development, testing, deployment, and maintenance of … machine learning solutions Monitor model performance post-deployment and iterate based on feedback Communicate with, and present results to, colleagues and stakeholders Present information using data visualization techniques Qualifications/Requirements A master’s degree (or equivalent) in a numerate discipline such as Statistics, Machine Learning, Computer Science, Engineering, or Physics. A doctorate is a plus but not required More ❯

or applying cutting-edge research to real-world challenges. This is a hands-on role where you'll contribute across the entire ML life cycle, from data preprocessing and model training to production deployment and performance tuning. If you're excited about solving complex problems with AI and thrive in a fast-paced, collaborative environment, we'd love to … hear from you. Responsibilities: Model Development: Design, develop, and deploy AI and machine learning models to solve business challenges. Data Analysis: Work with large datasets to extract insights and train machine learning models. Algorithm Implementation: Implement and optimize algorithms for AI applications, ensuring efficiency and scalability. Research: Stay up-to-date with the latest advancements in AI and machine … learning, and apply these to ongoing projects. Collaboration: Collaborate with data scientists, software engineers, and other stakeholders to integrate AI solutions into products and services. Testing and Validation: Test and validate AI models to ensure accuracy, robustness, and reliability. Deployment: Manage the deployment of AI models in production environments, monitoring performance and making necessary adjustments. Documentation: Document AI models More ❯

learning, and data science, particularly in the LLM field, and share knowledge with the team. Contribute to the creation of AI research pipelines, ensuring high data quality standards, rigorous model validation, and comprehensive performance evaluation. Mentor and guide junior engineers and researchers, fostering a culture of innovation and collaboration. Qualifications Master's degree in Computer Science, Artificial Intelligence More ❯

learning, and data science, particularly in the LLM field, and share knowledge with the team. Contribute to the creation of AI research pipelines, ensuring high data quality standards, rigorous model validation, and comprehensive performance evaluation. Mentor and guide junior engineers and researchers, fostering a culture of innovation and collaboration. Qualifications Master's degree in Computer Science, Artificial Intelligence More ❯

Job Description Job Purpose ICE Clear Europe is seeking a Quantitative Risk Manager to join its Model Risk Management team. This role is responsible for validating and monitoring risk models used in the clearing house, ensuring their accuracy, robustness, and compliance with regulatory standards. The position involves end-to-end model risk assessment across initial margin, add-ons … and stress testing frameworks, with a focus on market, credit, and liquidity risk. This is an exciting opportunity for a technical expert looking for broader model and management exposure in a collaborative and flat organizational structure. Responsibilities Conduct independent validation of risk and pricing models and review of stress testing frameworks, including conceptual soundness, assumption reasonableness, and performance … benchmarking. Continuously monitor model performance and review first-line risk management monitoring approaches. Document validation findings, communicate risks, and recommend improvements. Provide guidance on model usage and act as a key stakeholder liaison for new models and changes. Stay updated on evolving market practices, regulatory requirements, and quantitative methodologies. Mentor and support junior team members. Knowledge and More ❯

the first 25 applicants Get AI-powered advice on this job and more exclusive features. Job Purpose ICE Clear Europe is seeking a Quantitative Risk Manager to join its Model Risk Management team. This role is responsible for validating and monitoring risk models used in the clearing house, ensuring their accuracy, robustness, and compliance with regulatory standards. The position … involves end-to-end model risk assessment across initial margin, add-ons, and stress testing frameworks, with a focus on market, credit, and liquidity risk. This is an exciting opportunity for a technical expert looking for broader model and management exposure in a collaborative and flat organizational structure. Job Purpose ICE Clear Europe is seeking a Quantitative Risk … Manager to join its Model Risk Management team. This role is responsible for validating and monitoring risk models used in the clearing house, ensuring their accuracy, robustness, and compliance with regulatory standards. The position involves end-to-end model risk assessment across initial margin, add-ons, and stress testing frameworks, with a focus on market, credit, and liquidity More ❯