1 to 25 of 158 Statistical Modelling Jobs in the UK

in London. They work locally but operate Globally with offices across the globe. A Senior Sports Quantitative Modeller is a senior individual contributor and an expert in mathematical and statistical modelling, specifically for sports betting. This role is responsible for deriving new markets, understanding complex statistical distributions, and building robust, accurate quantitative models, particularly for Bet Builder … conception to deployment, and providing technical guidance to junior modellers. This role reports directly to the Data Science Manager and is an integral part of our Data Science - Core Modelling function. Key Responsibilities: Design, develop, and implement advanced mathematical and statistical models for sports betting, with a primary focus on deriving new markets and enhancing existing offerings. Possess … a deep understanding of complex statistical distributions and leverage techniques such as Monte Carlo simulations in model development. Rigorously back test and validate models to ensure their robustness, accuracy, and profitability in real-world betting scenarios. Drive and lead quantitative modelling initiatives, with a particular focus on BetBuilder products, from initial concept through to production deployment. Operate with More ❯

in London. They work locally but operate Globally with offices across the globe. A Senior Sports Quantitative Modeller is a senior individual contributor and an expert in mathematical and statistical modelling, specifically for sports betting. This role is responsible for deriving new markets, understanding complex statistical distributions, and building robust, accurate quantitative models, particularly for Bet Builder … conception to deployment, and providing technical guidance to junior modellers. This role reports directly to the Data Science Manager and is an integral part of our Data Science - Core Modelling function. Key Responsibilities: Design, develop, and implement advanced mathematical and statistical models for sports betting, with a primary focus on deriving new markets and enhancing existing offerings. Possess … a deep understanding of complex statistical distributions and leverage techniques such as Monte Carlo simulations in model development. Rigorously back test and validate models to ensure their robustness, accuracy, and profitability in real-world betting scenarios. Drive and lead quantitative modelling initiatives, with a particular focus on BetBuilder products, from initial concept through to production deployment. Operate with More ❯

annum Location: UK Hybrid - With occasional travel to a hub Contract Type : Permanent (full time) Our Team IFRS9 and Stress Testing team work at the intersection of Data Science, Statistical Modelling and Economics within Virgin Money's Model Risk and Analytics function. We use data to support the business through model building used for regulatory reporting or providing … actionable insights from the data. We draw on statistical modelling techniques to produce the best possible predictive models. What you'll be doing Operate as a modelling expert across all modelling projects and wider business development within IFRS9 and ST team. Lead the scoping, design, development, validation and implementation of credit risk models for unsecured Retail … portfolios, in line with Bank standards and regulatory compliance requirements. Independently leading statistical analysis to ensure the unsecured Retail portfolios loss provision models currently in operation remain robust and fit for purpose. Leading key discussions and presenting analytical findings and solutions to a range of stakeholders both within and external to the modelling area. Provide first-class model More ❯

work in a neo-bank/fintech-style environment, using cutting-edge technology to analyze terabytes of data and support high-impact investment decisions. Responsibilities: Develop, implement, and validate statistical and machine learning models for analysing non-performing loan portfolios. Collaborate with cross-functional teams—including credit, collections, and data engineering—to translate business objectives into robust analytical solutions. … Build software using modern technology to enable investing and asset management at scale. Apply Bayesian modelling and probabilistic programming techniques to address uncertainty and improve prediction accuracy. Analyse large-scale datasets to identify key drivers, trends, and early warning signals within NPL portfolios. Clearly communicate model results, insights, and recommendations to stakeholders, including both technical and non-technical audiences. … Stay current with advances in statistical modelling, machine learning, and data science, continuously evaluating and integrating new techniques and tools. Requirements: University degree in a STEM field (e.g., Mathematics, Statistics, Computer Science, Engineering, Physics, Economics); advanced degree preferred. Strong expertise in statistical modelling, Bayesian inference, and machine learning. Proficient in Python (using libraries such as NumPy More ❯

work in a neo-bank/fintech-style environment, using cutting-edge technology to analyze terabytes of data and support high-impact investment decisions. Responsibilities: Develop, implement, and validate statistical and machine learning models for analysing non-performing loan portfolios. Collaborate with cross-functional teams—including credit, collections, and data engineering—to translate business objectives into robust analytical solutions. … Build software using modern technology to enable investing and asset management at scale. Apply Bayesian modelling and probabilistic programming techniques to address uncertainty and improve prediction accuracy. Analyse large-scale datasets to identify key drivers, trends, and early warning signals within NPL portfolios. Clearly communicate model results, insights, and recommendations to stakeholders, including both technical and non-technical audiences. … Stay current with advances in statistical modelling, machine learning, and data science, continuously evaluating and integrating new techniques and tools. Requirements: University degree in a STEM field (e.g., Mathematics, Statistics, Computer Science, Engineering, Physics, Economics); advanced degree preferred. Strong expertise in statistical modelling, Bayesian inference, and machine learning. Proficient in Python (using libraries such as NumPy More ❯

data systems (e.g., MRI, Northgate, Civica, or Orchard) and the ability to apply advanced analytics to operational challenges in housing (desirable). Demonstrated experience in using machine learning, predictive modelling, and statistical analysis to solve real-world problems. Expertise in statistical modelling, predictive analytics, clustering, classification, and regression techniques. Strong background in data mining, pattern recognition More ❯

analysis SQL for querying and manipulation of structured customer and campaign data (mandatory) MS Excel including pivot tables, VLOOKUP, Power Query (mandatory) Python or R for advanced data manipulation, statistical analysis and modelling (mandatory) Data visualisation Power BI and/or Looker Studio for creating dynamic dashboards and reports in context of marketing KPIs (mandatory) Forecasting, modelling and predictive analytics (highly desirable) Predictive/statistical modelling Time series analysis Forecasting marketing metrics such as response, conversion and churn rate using historical campaign data Data-driven business casing and budgeting Marketing performance (mandatory) Good knowledge of forecasting techniques, marketing performance/channel metrics, and KPI frameworks and targets Campaign planning and forecasting Campaign evaluation with … discipline Professional certifications in data analytics, forecasting, or marketing analytics (e.g., Google Analytics, Microsoft Power BI, Tableau, CIM, or similar) (highly desirable) Familiarity with tools or languages used for statistical forecasting (e.g., R, Python More ❯

analysis SQL for querying and manipulation of structured customer and campaign data (mandatory) MS Excel including pivot tables, VLOOKUP, Power Query (mandatory) Python or R for advanced data manipulation, statistical analysis and modelling (mandatory) Data visualisation Power BI and/or Looker Studio for creating dynamic dashboards and reports in context of marketing KPIs (mandatory) Forecasting, modelling and predictive analytics (highly desirable) Predictive/statistical modelling Time series analysis Forecasting marketing metrics such as response, conversion and churn rate using historical campaign data Data-driven business casing and budgeting Marketing performance (mandatory) Good knowledge of forecasting techniques, marketing performance/channel metrics, and KPI frameworks and targets Campaign planning and forecasting Campaign evaluation with … discipline Professional certifications in data analytics, forecasting, or marketing analytics (e.g., Google Analytics, Microsoft Power BI, Tableau, CIM, or similar) (highly desirable) Familiarity with tools or languages used for statistical forecasting (e.g., R, Python More ❯

driving customer decisions. The team leverages technology and AI to enhance, not replace, human creativity and insight. Responsibilities - Lead end-to-end execution of complex data science projects integrating statistical modelling, machine learning (ML), and deep learning (DL). - Collaborate closely with cross-functional teams (product, engineering, business stakeholders) to deliver data-driven solutions. - Design and conduct hypothesis … testing (A/B and multivariate testing) and apply causal inference methodologies. - Perform exploratory data analysis, feature engineering, and develop models across traditional statistical and deep learning architectures. - Establish modelling frameworks with statistical quality control, detecting drift and decay. - Drive best practices for model explainability (e.g., SHAP/LIME) and scalable ML systems (including generative AI, NLP … data science with at least 2 years leading teams. - Proven success in production deployment of ML/LLM/NLP/CV models. - Strong understanding of machine learning fundamentals, statistical inference, and model evaluation. - Advanced proficiency in SQL (e.g., PostgreSQL, ELT/ETL) and Python (PyTorch, LightGBM, Scikit-learn). - Experience with modern AI concepts: prompt engineering, embeddings, vector More ❯

driving customer decisions. The team leverages technology and AI to enhance, not replace, human creativity and insight. Responsibilities - Lead end-to-end execution of complex data science projects integrating statistical modelling, machine learning (ML), and deep learning (DL). - Collaborate closely with cross-functional teams (product, engineering, business stakeholders) to deliver data-driven solutions. - Design and conduct hypothesis … testing (A/B and multivariate testing) and apply causal inference methodologies. - Perform exploratory data analysis, feature engineering, and develop models across traditional statistical and deep learning architectures. - Establish modelling frameworks with statistical quality control, detecting drift and decay. - Drive best practices for model explainability (e.g., SHAP/LIME) and scalable ML systems (including generative AI, NLP … data science with at least 2 years leading teams. - Proven success in production deployment of ML/LLM/NLP/CV models. - Strong understanding of machine learning fundamentals, statistical inference, and model evaluation. - Advanced proficiency in SQL (e.g., PostgreSQL, ELT/ETL) and Python (PyTorch, LightGBM, Scikit-learn). - Experience with modern AI concepts: prompt engineering, embeddings, vector More ❯

safely and with less environmental impact. .# Job Description # Key Responsibilities May Include: Collaborate with key stakeholders to identify business challenges, translating ambiguous problems into structured analyses using statistical modelling and machine learning algorithms. Lead the selection, validation, and optimization of models to discover meaningful patterns and insights, ensuring models remain relevant, reliable, and scalable. Drive continuous … and resource allocation. Contribute to the creation of business cases by quantifying the impact of data science solutions on supply chain KPIs and financial outcomes. Focus on data science modelling in close collaboration with the Data Engineering team, which is responsible for data wrangling, clean-up, and transformation to provide high-quality data for analysis. Experience: Proven track record … designing, developing, and deploying advanced machine learning and statistical models in complex supply chain environments. Extensive hands-on experience collaborating with data engineering teams for data wrangling, cleaning, and transformation to ensure high-quality datasets for modelling. Proficient in programming languages such as Python, R, and SQL for data analysis and model development. Experience working with cloud computing platforms More ❯

users to identify patterns in usage, engagement, and retention. Supporting retention strategies through data-driven recommendations across push, email, and in-app activity. Collaborating with data science on predictive modelling (e.g., behavioural modelling, risk likelihood). Ensuring data quality and readiness in partnership with engineering and tagging teams. Leading and developing a team of digital analysts, providing coaching … direction, and technical guidance. TECHNICAL ENVIRONMENT Tools: SQL, GA4, BigQuery Languages: Python, R Focus Areas: Digital analytics, predictive modelling, product behaviour, insight generation No tagging responsibilities — handled by a specialist team Leadership-first role, but with enough technical depth to guide high-quality analysis SKILLS & EXPERIENCE REQUIRED Essential: Experience in analytics, digital insights, or data science. Strong background in … digital analytics, predictive modelling, or behavioural insights. Ability to translate complex analytical outputs into clear business recommendations. Experience managing or mentoring analysts. Comfortable working across app and web journeys, particularly around engagement and retention. Strong communication skills with the ability to influence stakeholders at multiple levels. High energy, proactive mindset, and the ability to bring momentum to projects. Desirable More ❯

users to identify patterns in usage, engagement, and retention. Supporting retention strategies through data-driven recommendations across push, email, and in-app activity. Collaborating with data science on predictive modelling (e.g., behavioural modelling, risk likelihood). Ensuring data quality and readiness in partnership with engineering and tagging teams. Leading and developing a team of digital analysts, providing coaching … direction, and technical guidance. TECHNICAL ENVIRONMENT Tools: SQL, GA4, BigQuery Languages: Python, R Focus Areas: Digital analytics, predictive modelling, product behaviour, insight generation No tagging responsibilities — handled by a specialist team Leadership-first role, but with enough technical depth to guide high-quality analysis SKILLS & EXPERIENCE REQUIRED Essential: Experience in analytics, digital insights, or data science. Strong background in … digital analytics, predictive modelling, or behavioural insights. Ability to translate complex analytical outputs into clear business recommendations. Experience managing or mentoring analysts. Comfortable working across app and web journeys, particularly around engagement and retention. Strong communication skills with the ability to influence stakeholders at multiple levels. High energy, proactive mindset, and the ability to bring momentum to projects. Desirable More ❯

users to identify patterns in usage, engagement, and retention. Supporting retention strategies through data-driven recommendations across push, email, and in-app activity. Collaborating with data science on predictive modelling (e.g., behavioural modelling, risk likelihood). Ensuring data quality and readiness in partnership with engineering and tagging teams. Leading and developing a team of digital analysts, providing coaching … direction, and technical guidance. TECHNICAL ENVIRONMENT Tools: SQL, GA4, BigQuery Languages: Python, R Focus Areas: Digital analytics, predictive modelling, product behaviour, insight generation No tagging responsibilities - handled by a specialist team Leadership-first role, but with enough technical depth to guide high-quality analysis SKILLS & EXPERIENCE REQUIRED Essential: Experience in analytics, digital insights, or data science. Strong background in … digital analytics, predictive modelling, or behavioural insights. Ability to translate complex analytical outputs into clear business recommendations. Experience managing or mentoring analysts. Comfortable working across app and web journeys, particularly around engagement and retention. Strong communication skills with the ability to influence stakeholders at multiple levels. High energy, proactive mindset, and the ability to bring momentum to projects. Desirable More ❯

as Excel, Python (or R) Bachelor's degree ideally in a business or quantitative subject (e.g. computer science, mathematics, engineering, science, economics or finance). A good understanding of statistical modelling knowledge or any machine learning technique knowledge (such as hypothesis testing, regression, logistic regression, random forest, etc.) Good stakeholder management experience. Comfortable presenting to senior leadership and More ❯

strategy, and optimise commercial performance. This is a high-impact role for a proactive, solutions-oriented professional who can prioritise based on business needs. Key Responsibilities: Deliver analytics and statistical modelling to shape commercial strategy and support measurable business outcomes. Prepare sales and product forecasts, including OTE projections, to aid planning and resource allocation. Track, analyse, and report … like you to have: 37 years of experience in data analytics, business intelligence, or commercial analysis. Strong proficiency in Excel and experience with BI platforms (Power BI, Tableau) and statistical modeling. Ability to translate data insights into actionable recommendations for sales, finance, and product teams. Commercial awareness, analytical mindset, and strong problem-solving skills. Excellent communication and stakeholder management More ❯

strong plus. Experience Required skills Hands-on experience building and deploying ML models in Python SQL skills and familiarity with AWS for deployment and data workflows. Strong knowledge of statistical modelling, anomaly detection, clustering, and supervised/unsupervised learning Experience working with large-scale data Proven success collaborating with product and engineering teams to ship ML-based features More ❯

strong plus. Experience Required skills Hands-on experience building and deploying ML models in Python SQL skills and familiarity with AWS for deployment and data workflows. Strong knowledge of statistical modelling, anomaly detection, clustering, and supervised/unsupervised learning Experience working with large-scale data Proven success collaborating with product and engineering teams to ship ML-based features More ❯

within the organisation, lead complex projects, and provide advanced analytical insights that drive measurable business outcomes. Key Responsibilities Model Development & Deployment: Design, build, and deploy scalable machine learning and statistical models for use cases such as pricing optimisation, customer behaviour prediction, and risk modelling. Data Exploration & Feature Engineering: Work with structured and unstructured data to uncover patterns and develop More ❯

within the organisation, lead complex projects, and provide advanced analytical insights that drive measurable business outcomes. Key Responsibilities Model Development & Deployment: Design, build, and deploy scalable machine learning and statistical models for use cases such as pricing optimisation, customer behaviour prediction, and risk modelling. Data Exploration & Feature Engineering: Work with structured and unstructured data to uncover patterns and develop More ❯

Senior Data Scientist - Asset Risk Modelling London - Hybrid, 3 days in office£85,000 - £90,000 + Bonus + Great Pension + Private Healthcare + 28 days Holiday + Hybrid Working This is a brilliant opportunity for a Senior Data Scientist with strong experience in model risk management, pricing, and insurance to join a market-leading organisation during a … SMR, Insurance Lease Pricing, Economic Capital, and Customer Pricing. As part of their continued expansion, they are now seeking a talented Senior Data Scientist to join the Asset Risk Modelling Team and help shape the future of their modelling capabilities.In this role, you will take ownership of developing, maintaining, and enhancing advanced forecasting and pricing models that underpin … critical business decisions. Working closely with the Modelling Manager and wider stakeholders, you'll ensure the robustness and transparency of all models, while continuously improving methodologies, data use, and analytical processes. You will also play a key role in delivering the model risk management framework across the Asset Risk function.The ideal candidate will be an experienced Data Scientist/ More ❯

Senior Data Scientist - Asset Risk Modelling London - Hybrid, 3 days in office £85,000 - £90,000 + Bonus + Great Pension + Private Healthcare + 28 days Holiday + Hybrid Working This is a brilliant opportunity for a Senior Data Scientist with strong experience in model risk management, pricing, and insurance to join a market-leading organisation during a … SMR, Insurance Lease Pricing, Economic Capital, and Customer Pricing. As part of their continued expansion, they are now seeking a talented Senior Data Scientist to join the Asset Risk Modelling Team and help shape the future of their modelling capabilities. In this role, you will take ownership of developing, maintaining, and enhancing advanced forecasting and pricing models that … underpin critical business decisions. Working closely with the Modelling Manager and wider stakeholders, you'll ensure the robustness and transparency of all models, while continuously improving methodologies, data use, and analytical processes. You will also play a key role in delivering the model risk management framework across the Asset Risk function. The ideal candidate will be an experienced Data More ❯

supporting them post-deployment. Strong Python programming skills, including object-oriented design and proficiency with key ML libraries (e.g., PyTorch, TensorFlow, Scikit-Learn). Solid understanding of probability and statistical modeling to support robust model development and interpretation. Experience with cloud platforms (especially Azure and/or AWS) and modern deployment practices for scalable AI delivery. Proven ability to More ❯

Job Responsibilities: Collaborate with cross-functional teams to identify business problems and develop data-driven solutions. Provide technical mentorship and guidance to other data scientists. Design and implement algorithms, statistical models, and machine learning solutions to analyse complex data sets. Communicate analytical insights clearly to both technical and non-technical stakeholders. Engage with clients to understand requirements and convert … in data science. Experience Required: Proven experience as a Data Scientist with proficiency in Python and libraries such as NumPy, Pandas, Scikit-learn, Matplotlib, and Plotly. Strong background in statistical modelling, machine learning, and data mining, with experience working on time-series data. Knowledge of data engineering principles, including pipelines, databases, and infrastructure; AWS experience is advantageous. Experience More ❯

Skills Effective at people management Highly analytical, logical, creative thinker Proven ability to structure problems Able to refine and clarify vague requirements Experienced in delivering metrics, analyses, visualisations, and statistical models Ability to lead the direction of analytical projects Can prioritise workloads and manage tight deadlines Strong stakeholder management, influencing and interpersonal skills Ability to communicate and present to … statistics to summarise data, identify trends, forecast, and predict outcomes Project management and planning Highly effective team worker Strong business acumen and commercial awareness Technical skills: Data analysis, visualisation, statistical modelling, predictive analytics. Experience Essential Experience Analysing complex issues, packaging findings and presenting effectively to stakeholders Managing databases and/or blending data Designing data for management information More ❯