1 to 25 of 124 Model Validation Jobs in London

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

risk functions. Deliver scalable and efficient analytics infrastructure using C++ and Python , aligned with front-office requirements. Work closely with trading, structuring, and risk teams to provide high-performance model-driven tools and analytics. Contribute to the ongoing enhancement and modernization of the quantitative library and risk analytics platform. Ensure full documentation of models in accordance with internal governance … and regulatory expectations. Engage with model validation and risk control teams throughout the model approval and lifecycle management process. Provide analytical support and ensure robustness of models in production environments. Required Qualifications & Experience: Advanced degree (MSc/PhD) in a quantitative discipline such as Mathematics, Physics, Financial Engineering, or Computer Science. Demonstrated experience in a front-office … quantitative analytics or quant development role within a financial institution. Strong understanding of derivative pricing theory and volatility modelling techniques. Proficiency in C++ and Python for model development and numerical computation. Familiarity with structured software development practices, including version control, testing, and continuous integration. Excellent problem-solving skills, a high level of attention to detail, and strong written and More ❯

risk functions. Deliver scalable and efficient analytics infrastructure using C++ and Python , aligned with front-office requirements. Work closely with trading, structuring, and risk teams to provide high-performance model-driven tools and analytics. Contribute to the ongoing enhancement and modernization of the quantitative library and risk analytics platform. Ensure full documentation of models in accordance with internal governance … and regulatory expectations. Engage with model validation and risk control teams throughout the model approval and lifecycle management process. Provide analytical support and ensure robustness of models in production environments. Required Qualifications & Experience: Advanced degree (MSc/PhD) in a quantitative discipline such as Mathematics, Physics, Financial Engineering, or Computer Science. Demonstrated experience in a front-office … quantitative analytics or quant development role within a financial institution. Strong understanding of derivative pricing theory and volatility modelling techniques. Proficiency in C++ and Python for model development and numerical computation. Familiarity with structured software development practices, including version control, testing, and continuous integration. Excellent problem-solving skills, a high level of attention to detail, and strong written and More ❯

build, enhance and analyse mathematical models designed to optimize liquidity usage in the firm. Build quantitative tools to attribute, explain and perform scenario analyses on various liquidity metrics. Write model documents and execute model validation process in accordance with firm policy for quantitative models. Collaborate with non-engineers to explain model behaviour. Basic Qualifications Advanced degrees More ❯

house team, the candidateneeds to betruly, 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 developmentexperience isvery,highly desirable,we do have a team of engineers to support so exposurein thisspacewill be sufficient. Culture-wise,we're looking for a great … Mobilize the Enterprise . Key Responsibilities: Further improveexisting algorithms and develop net new DS features Design andcontribute tothe end-to-end machine learning pipeline from data collection, reprocessing to model training,simulation,evaluation, deploymentand experimentation/testing Implement and interpret explainability frameworks to provide clear insights into model decisions, ensuring transparency and compliance withWPPstandards 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 ofacademic researchandindustry advancements inoptimisation, plusAI More ❯

models and advanced analytics solutions. Collaborate cross-functionally to identify and prioritize data science opportunities. Build and mentor a high-performing data science team. Establish best practices for experimentation, model validation, and deployment. Communicate complex data insights to technical and non-technical stakeholders. Stay current with trends in data science, machine learning, and financial technologies. Requirements Bachelor’s More ❯

models and advanced analytics solutions. Collaborate cross-functionally to identify and prioritize data science opportunities. Build and mentor a high-performing data science team. Establish best practices for experimentation, model validation, and deployment. Communicate complex data insights to technical and non-technical stakeholders. Stay current with trends in data science, machine learning, and financial technologies. Requirements Bachelor’s More ❯

through advanced econometric modelling—including Bayesian Marketing Mix Modelling (MMM), Multi-Touch Attribution (MTA), and data-driven optimization strategies. Key Responsibilities Lead and manage data workflows: data extraction, transformation, validation, and exploratory analysis to ensure modelling-readiness. Build and refine Bayesian MMM models that capture the drivers of key marketing and commercial KPIs. Use Python (and optionally R) to More ❯

through advanced econometric modelling—including Bayesian Marketing Mix Modelling (MMM), Multi-Touch Attribution (MTA), and data-driven optimization strategies. Key Responsibilities Lead and manage data workflows: data extraction, transformation, validation, and exploratory analysis to ensure modelling-readiness. Build and refine Bayesian MMM models that capture the drivers of key marketing and commercial KPIs. Use Python (and optionally R) to More ❯

through advanced econometric modelling—including Bayesian Marketing Mix Modelling (MMM), Multi-Touch Attribution (MTA), and data-driven optimization strategies. Key Responsibilities Lead and manage data workflows: data extraction, transformation, validation, and exploratory analysis to ensure modelling-readiness. Build and refine Bayesian MMM models that capture the drivers of key marketing and commercial KPIs. Use Python (and optionally R) to More ❯

skills, and experience in derivatives modelling. Key Responsibilities: Support and improve internal risk models related to CVA for equity/volatility products (e.g. Corridor Variance Swaps) Document and test model outputs with a high degree of accuracy Liaise with front office, tech, and model validation teams to ensure successful production deployment Communicate complex model results to … exposure to structured or hybrid equity products Experience working with cross-asset or quantitative strategy teams Ability to work independently in a fast-paced, regulated environment An interest in model governance, compliance, and process robustness Why Join This Project? Contribute to high-impact equity derivatives risk models used globally Partner directly with front-office traders and quantitative teams Grow … your skills with exposure to modern tools and model deployment pipelines More ❯

for our Financial Services client. Key Responsibilities End-to-End AI Solution Delivery: Lead AI/ML initiatives from conceptual design to production deployment, including problem scoping, data acquisition, model development, validation, deployment, and monitoring. AI-Driven Marketing Insights: Develop predictive and generative models to support audience segmentation, personalization, channel optimization, lead scoring, and campaign measurement. Collaborative Development … Work closely with marketing strategists, data analysts, data engineers, and product owners to define use cases and deliver scalable solutions. Model Deployment & Monitoring: Deploy models using MLOps practices and tools (e.g., MLflow, Airflow, Docker, cloud platforms) ensuring performance, reliability, and governance compliance. Innovation & Research: Stay current on advancements in AI/ML and proactively bring forward new ideas, frameworks … techniques that can be applied to marketing use cases. Data Strategy: Collaborate with data engineering teams to ensure the availability of clean, structured, and enriched data pipelines required for model training and inference. Required Qualifications Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, Applied Mathematics, or a related field. 4+ years of experience in More ❯

for our Financial Services client. Key Responsibilities End-to-End AI Solution Delivery: Lead AI/ML initiatives from conceptual design to production deployment, including problem scoping, data acquisition, model development, validation, deployment, and monitoring. AI-Driven Marketing Insights: Develop predictive and generative models to support audience segmentation, personalization, channel optimization, lead scoring, and campaign measurement. Collaborative Development … Work closely with marketing strategists, data analysts, data engineers, and product owners to define use cases and deliver scalable solutions. Model Deployment & Monitoring: Deploy models using MLOps practices and tools (e.g., MLflow, Airflow, Docker, cloud platforms) ensuring performance, reliability, and governance compliance. Innovation & Research: Stay current on advancements in AI/ML and proactively bring forward new ideas, frameworks … techniques that can be applied to marketing use cases. Data Strategy: Collaborate with data engineering teams to ensure the availability of clean, structured, and enriched data pipelines required for model training and inference. Required Qualifications Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, Applied Mathematics, or a related field. 4+ years of experience in More ❯

invalid traffic filtering • Drive core business analytics and data science explorations to inform key business decisions and algorithm roadmap • Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation • Hire and develop top talent in machine learning and data science and accelerate the pace of innovation in the group … department functional experience Knowledge of a statistical analysis package such as R, Tableau, and high-level programming language (E.g. Python) used in the context of data analysis and statistical model building Strongly motivated by entrepreneurial projects and experienced in collaboratively working with a diverse team of engineers, analysts, and business management in achieving superior bottom line results Strong communication More ❯

probabilistic programming tools. Hands-on experience with classical ML and modern techniques, including deep learning , transformers , diffusion models , and ensemble methods . Solid understanding of feature engineering, dimensionality reduction, model construction, validation, and calibration. Experience with uncertainty quantification and performance estimation (e.g., cross-validation, bootstrapping, Bayesian credible intervals). Familiarity with database and data processing tools (e.g. More ❯

DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we More ❯

Head of Quantitative Risk Analytics – Quantitative Finance, Counterparty Credit Risk, Model Development, Model Validation, Risk Analysis, Python, R, SQL, Numerix, - City of London, Permanent A senior Quantitative Specialist is sought after by a Global Investment Bank to take ownership of their European Counterparty Credit Risk (CRR) modelling function, as part of the wider Risk Analytics group. In … this role, you will be responsible for managing the end-to-end modelling lifecycle, being responsible for methodology, model design and development, through to implementation and validation, helping support local Counterparty Credit Risk Management. This will be a multi-functional role, with responsibility for building and maintaining the modelling infrastructure and ecosystem, as well as undertaking quantitative research … to keep models up to date ensuring the business have access to accurate analytics. You will work closely with the business and other quantitative specialists for a cohesive model development process, including the implementation of highly accessible tools and dashboards for users to effectively undertake risk analysis. To be successful, you will demonstrate: Minimum of a Master’s degree More ❯

and strategy indices. Key Responsibilities Maintain and enhance internal risk models used in CVA calculations for volatility-sensitive products (e.g., Corridor Variance Swaps) Collaborate with Front Office, Technology, and Model Validation teams to deploy models Document, test, and validate model outputs Ensure model accuracy, robustness, and compliance Share best practices and support knowledge transfer across teams … mathematical and programming skills (C++, Python) Experience in derivatives modeling and front-office environments Knowledge of financial engineering and product structuring Strong analytical and communication skills High standards for model integrity and accuracy Desirable Self-motivated with attention to deadlines and quality Innovative mindset and proactive approach More ❯

that attribute spot metrics and project future requirement, produce quantitative analytics on historical metrics data. • Build quantitative tools to explain and perform scenario analyses on various liquidity metrics. • Write model documents and execute model validation process in accordance with firm policy for quantitative models. • Collaborate with non-engineers to explain model behavior. QUALIFICATIONS • Bachelor's degree … Python, C++ or Java. • Developing financial pricing models in any asset class. • Maintaining a production code base and daily production processes. • Preparing and submitting technical documents to support the validation of mathematical models. • Working with techniques of optimization, statistical analysis, including parameter estimation. ABOUT GOLDMAN SACHS At Goldman Sachs, we commit our people, capital and ideas to help our More ❯