1 to 25 of 89 Statistical Modelling Jobs in London

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

experience in Data Science or a similar quantitative role, with a strong track record of influencing product strategy. Expert-level proficiency in Python and SQL, with deep experience in statistical modelling, experimentation, and a wide range of machine learning techniques. Strong product acumen and experience defining and operationalising product and feature-level metrics. Demonstrated ability to drive strategic More ❯

experience in Data Science or a similar quantitative role, with a strong track record of influencing product strategy. Expert-level proficiency in Python and SQL, with deep experience in statistical modelling, experimentation, and a wide range of machine learning techniques. Strong product acumen and experience defining and operationalising product and feature-level metrics. Demonstrated ability to drive strategic More ❯

career the that of a Data Scientist. This builds on the knowledge of the Data+ certification and enables you to demonstrate your knowledge in advanced data processing, cleaning, and statistical modelling concepts. You will demonstrate your knowledge of machine learning, industry trends and use of specialised data science applications. You will also apply mathematical and statistical methods More ❯

with production-grade engineering. Key Responsibilities Design, train, and deploy state-of-the-art machine learning and deep learning models to predict health outcomes and disease progression. Apply advanced statistical and causal inference methods (e.g. survival analysis, time-to-event modelling, propensity scoring, Mendelian randomisation). Analyse and integrate multi-omics and clinical datasets to uncover novel biomarkers … Biology, Statistics, Bioinformatics, or related quantitative field. Background in cardiovascular, cardiometabolic, or precision medicine research. Proven experience developing deep learning models using Python, PyTorch, or TensorFlow. Strong understanding of statistical modelling, causal reasoning, and predictive analytics. Demonstrated experience working with large-scale health, genomic, or biobank datasets (e.g. UK Biobank, All of Us, Our Future Health). Exposure More ❯

with production-grade engineering. Key Responsibilities Design, train, and deploy state-of-the-art machine learning and deep learning models to predict health outcomes and disease progression. Apply advanced statistical and causal inference methods (e.g. survival analysis, time-to-event modelling, propensity scoring, Mendelian randomisation). Analyse and integrate multi-omics and clinical datasets to uncover novel biomarkers … Biology, Statistics, Bioinformatics, or related quantitative field. Background in cardiovascular, cardiometabolic, or precision medicine research. Proven experience developing deep learning models using Python, PyTorch, or TensorFlow. Strong understanding of statistical modelling, causal reasoning, and predictive analytics. Demonstrated experience working with large-scale health, genomic, or biobank datasets (e.g. UK Biobank, All of Us, Our Future Health). Exposure More ❯

familiarity with NoSQL Comfortable using Git and Jupyter notebooks A practical, problem-solving mindset with a focus on clean, reliable delivery Nice to have: Experience with R or other statistical tools Exposure to model deployment/MLOps environments Familiarity with high-volume digital products or gaming/sports analytics More ❯

familiarity with NoSQL Comfortable using Git and Jupyter notebooks A practical, problem-solving mindset with a focus on clean, reliable delivery Nice to have: Experience with R or other statistical tools Exposure to model deployment/MLOps environments Familiarity with high-volume digital products or gaming/sports analytics More ❯