1 to 25 of 33 Permanent Statistical Modelling Jobs in the City of 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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

that deliver commercial value Lead the design, development, and deployment of predictive and optimisation models across multiple industries Own the end-to-end ML pipeline: data exploration, feature engineering, modelling, evaluation, and deployment Collaborate with data engineers and MLOps professionals to ensure scalable, production-grade solutions Conduct rigorous A/B testing and performance measurement to validate impact Act … measurable impact, not just model accuracy. Requirements: Proven experience applying data science in commercial settings Proven ability to lead data science projects from concept to production Deep understanding of statistical modelling, predictive analytics, and optimisation techniques Comfortable working with cross-functional teams, including engineers, product leads, and client stakeholders Bachelor’s or Master’s degree in a quantitative More ❯

that deliver commercial value Lead the design, development, and deployment of predictive and optimisation models across multiple industries Own the end-to-end ML pipeline: data exploration, feature engineering, modelling, evaluation, and deployment Collaborate with data engineers and MLOps professionals to ensure scalable, production-grade solutions Act as a technical lead within project teams, mentoring mid-level data scientists … by measurable impact, not just model accuracy. Proven experience applying data science in commercial settings Proven ability to lead data science projects from concept to production Deep understanding of statistical modelling, predictive analytics, and optimisation techniques Comfortable working with cross-functional teams, including engineers, product leads, and client stakeholders Bachelor’s or Master’s degree in a quantitative More ❯

governments. Following a $100M+ Series B and full-stack insurance licensing, they are expanding their London presence. As a Senior Scientist, you’ll operate at the intersection of data, modelling, and market strategy, supporting bespoke coverage for weather-related events. You’ll collaborate with commercial, technical, and research teams to shape tailored, data-led insurance products for clients worldwide. … a data science, actuarial, or underwriting environment Strong quantitative background with a degree in maths, statistics, physics, or similar Proficiency in Python (e.g. pandas, scikit-learn) and comfort with statistical modelling Interest or prior experience in parametric insurance and/or climate risk modelling Track record of managing technical projects or small teams is a plus Strong More ❯

of experience in data analysis and ETL development. Strong proficiency in Teradata SQL and Informatica PowerCenter. Experience building and validating risk models using SAS and Python. Solid understanding of statistical techniques and risk modeling frameworks. Familiarity with data governance and compliance standards. Excellent problem-solving and communication skills. ABOUT CAPGEMINI Capgemini is a global business and technology transformation partner More ❯

writing, client presentations, and proposal delivery. Skills and Experience Required Strong capabilities across a range of AI techniques, including supervised and unsupervised learning, generative AI, deep learning, graph analytics, statistical modelling, time-series forecasting, geospatial analysis, NLP, sentiment analysis, and pattern detection. Excellent communication skills, with the ability to present complex technical concepts clearly and confidently to non More ❯

a good understanding of architecture, security, and scaling Hands-on experience with Docker and Kubernetes in real-world engineering workflows Solid grasp of ML fundamentals: supervised/unsupervised learning, statistical modelling, evaluation A pragmatic approach to engineering capable of balancing speed, risk, and delivery in complex environments Excellent communication and collaboration skills, especially in client-facing settings Prior More ❯

FMCG. They pride ourselves on nurturing talent and fostering a culture of innovation and inclusion. As a Senior Consultant, you will: Lead projects to deliver actionable insights using advanced statistical modeling and research integration. Drive business development , contributing to proposals and pitches. Collaborate on product development and innovate new analytical approaches. Work with diverse datasets, ensuring quality control and More ❯

acquisition, retention, and loyalty strategy, working cross-functionally with Marketing and Product teams to turn insight into commercial impact. Responsibilities include Owning customer analytics projects such as segmentation, churn modelling, LTV, and lifecycle optimisation Designing and analysing A/B and multivariate tests to shape acquisition and retention strategies Using SQL and dashboarding tools such as Tableau or Looker … environments Ability to translate analytical outputs into strategic recommendations and tell clear data stories Experience designing or analysing A/B testing and customer lifecycle analytics Python skills for statistical modelling or automation are an advantage This role offers a hybrid setup (typically two days per week in the London office), a base salary of approximately More ❯

defining approaches to complex data challenges, guiding enterprise-scale AI applications, and mentoring a team of talented data scientists. What You’ll Bring 🔥 Deep expertise in machine learning and statistical modelling ⚙️ Skilled with core data science tools (NumPy, Pandas, Scikit-Learn) and deep learning frameworks (PyTorch, TensorFlow) 💡 Proven track record delivering impactful AI/ML projects (e.g. forecasting More ❯

orchestration engines in AWS Integrate diverse datasets, including market, fundamental, and weather data Develop analytical and visualization tools (e.g., Dash/Plotly) Partner with analysts to design and implement statistical models Contribute to architectural design of scalable analytics systems Manage deployment and operationalization of workloads in AWS (S3, ECS, Postgres) Promote engineering best practices including Git-based version control More ❯

effectively to both technical and non-technical stakeholders, including coaches, scouts, analysts, and traders. Skills & Attributes Strong background in Python, with experience in data manipulation (Pandas/Polars) and statistical modelling. Industry experience in a data science role focussing on sports analytics. Knowledge of advanced football metrics (possession value models highly desirable). A deep passion for football combined More ❯

designing, training, and deploying machine learning models in production. Strong proficiency in Python and key ML frameworks ( PyTorch , TensorFlow , scikit-learn ). Deep understanding of machine learning algorithms and statistical modelling. Ability to work independently while collaborating effectively within technical teams. Excellent analytical , problem-solving , and communication skills. NICE TO HAVE: Hands-on experience with LLMs and Natural Language More ❯

designing, training, and deploying machine learning models in production. Strong proficiency in Python and key ML frameworks ( PyTorch , TensorFlow , scikit-learn ). Deep understanding of machine learning algorithms and statistical modelling. Ability to work independently while collaborating effectively within technical teams. Excellent analytical , problem-solving , and communication skills. NICE TO HAVE: Hands-on experience with LLMs and Natural Language More ❯

fan engagement. If you're passionate about using advanced data science to drive real-world impact in sport, this is the role for you. Key Responsibilities Develop and apply statistical models and algorithms to analyse player performance, match outcomes, and tactical insights Collect, clean, and process football-related data from diverse sources Build clear, compelling visualizations and deliver insights … Background 3+ years of industry experience as a Data Scientist , plus a strong academic foundation Python Data Science Stack : Advanced proficiency in Python , including Pandas, NumPy and scikit-learn. Statistical & Machine Learning Modelling : Experience with a variety of ML techniques ( regression, classification, clustering, time-series forecasting ) Experience with deep learning frameworks such as Keras or PyTorch Model Deployment … into actionable insights for coaches, analysts, and execs Highly Desirable Skills Football Analytics Experience : Familiarity with football-specific datasets (event, tracking, positiona l), and libraries like mplsoccer Advanced MLOps & Modelling : Experience with the Vertex AI ecosystem, especially pipelines, and advanced techniques such as player valuation , tactical modelling , etc. Bayesian Modelling : Knowledge of probabilistic programming (e.g., PyMC ) for More ❯

Leading pharmaceutical client now requires a Senior Data Scientist to drive marketing effectiveness via marketing mix modelling and other models. This role will oversee data collection, extraction, manipulation, analysis and validation ensuring data is ready for modelling. You will work closely with the Data Science team to build base models according to project specifications and ensure the robustness and … and previous experience with R programming and ideally with MMM models. Strong experience with Pymc is essential. Experience with Regression based models in an MMM context. Strong understanding of statistical modelling/Machine Learning techniques. Experience with probabilistic programming and Bayesian methods Good working knowledge of cloud-based data science frameworks. This is a 6 month contract position More ❯