1 to 25 of 28 Permanent Bayesian Methods Jobs in the UK excluding London

communication skills. Beneficial to have: Experience in a similar role in a professional football environment. Experience with Time-Series Analysis & Forecasting. Experience with Bayesian inference/MCMC. What's in it for you? We are committed to ensuring everyone can flourish in their roles. To achieve this ...

fundamentals Advanced data skills: statistical modelling, machine learning, time-series forecasting, demand prediction, optimisation, inventory management Experience with dynamic pricing, stochastic processes, or Bayesian inference is a plus Preferred Experience Background in pricing, forecasting or optimisation roles within airlines, hospitality, car rentals, e-commerce, or similar dynamic ...

and machine learning. Statistics & Math: Deep understanding of statistical inference, regression analysis, probability theory, decision trees, and hypothesis testing (p-values, confidence intervals, Bayesian testing etc.). Data Visualization: Hands‐on experience building interactive dashboards using tools like Power BI, Looker, or Python libraries. Experience: 3+ years ...

experimental data, and in your penultimate year of study. Experience working with datasets using Python, R, SAS, MATLAB, or other statistical software. Experience with methods of statistical data analysis (e.g., linear models, hypothesis testing, multi-level models, Bayesian methods, etc.). Preferred qualifications: Experience with … search techniques. Produce quantitative and qualitative modeling of business dynamics, user behavior, and more. Identify areas for further investigation as well as creating innovative methods of analysis. Please complete your application before 23rd October 2026. We encourage you to apply as early as possible as we review applications ...

pricing models. Turning pricing opportunities into measurable hypotheses, experiments and production changes. Evaluating pricing performance through backtesting, online testing and commercial metrics. Developing Bayesian, multi-armed bandit and other optimisation approaches for dynamic pricing. Working with commercial, finance and product teams to understand pricing performance and recommend … code, testing and code review, alongside strong SQL. A solid grounding in machine learning techniques such as gradient boosting, GLMs, Bayesian methods and multi-armed bandits, or comparable experimentation and optimisation methods. Experience designing, running and evaluating pricing experiments using both commercial and statistical measures. ...

what questions should be explored next. Key Responsibilities & Skills ** Develop predictive models and forecasts to identify potential future outcomes and emerging trends ** Applying Bayesian modelling, uncertainty estimation and statistical validation ** Carry out segmentation and subgroup analysis to identify meaningful differences and patterns ** Analyse longitudinal and time-series ...

technical leadership: mentoring, setting standards, and influencing scientific and technical direction beyond your own projects. Beneficial Skills Practical experience with active learning, Bayesian optimisation, conformal prediction, or uncertainty quantification in iterative real‐world loops. Familiarity with retrosynthesis ML, computer‐aided synthesis planning (CASP), or reaction‐condition/ ...

required to obtain UK Security Clearance. Desirable Experience Experience with any of the following would be beneficial: Statistical modelling, Bayesian methods, uncertainty estimation, or probabilistic techniques. Synthetic data generation, multi-modal AI, or action recognition. Machine learning development tools, experiment tracking, dataset management, or CI/ ...

explain complex technical concepts clearly Strong programming skills in Python, with experience in C++ for production or performance-critical systems Desirable: Experience with Bayesian inference, uncertainty estimation, probabilistic modelling, or state estimation Familiarity with custom loss functions, action recognition, synthetic data, or multi-modal neural networks ...

seeking a Principal Scientist within the Cheminformatics (CIX) group. You will leverage large proprietary internal datasets to develop, integrate and embed advanced computational methods and predictive in silico models that accelerate the discovery of medicines. With a focus on machine learning, cheminformatics and computational chemistry methodologies, you will help … guidance that is accessible to non-experts.Contribute to and validate production-quality code implementing state-of-the-art cheminformatics, computational chemistry and quantum chemistry methods that accelerate and improve decision-making on drug discovery programmes.Prepare and present results of key validation experiments, details of capability builds, and developments ...

rich environments Strong statistical knowledge Experience with product analytics and behavioural tracking Experience mentoring or coaching other analysts Nice to have Knowledge of Bayesian or Frequentist experimentation approaches Experience with event-level analytics platforms Predictive analytics exposure The team You'll report into the Analytics Team Lead ...

Make your mark for patientsWe are looking for a Principal Computational Methodologist who is curious, collaborative, and impact-driven to join our Advanced Methods and Data Science team within Biometrics and Data Science, based in any of our Brussels (Belgium), Slough (UK) or Raleigh (US).About the roleYou will … help shape how advanced AI, machine learning, and computational methods support clinical development. You will design, validate, and deploy models that help teams make evidence-based decisions in areas such as biomarker discovery, clinical trial design, operational efficiency, and patient experience. You will guide scientific thinking, turn complex methods ...

senior specialist role focused on understanding the drivers of demand, outcomes, variation, inequalities and cost across our population. You will apply advanced analytical methods to complex system questions and translate findings into clear intelligence that supports senior decision making.The role will work closely with colleagues across analytics, BI, data … analysis, evidence generation and evaluation rather than routine reporting or dashboard development.Main duties of the jobYou will lead complex analytical and evaluative projects, applying methods such as regression, forecasting, segmentation, risk stratification, causal inference, impact evaluation, cost-effectiveness analysis and statistical modelling where appropriate.You will be experienced in working ...

sitting at the intersection of modern machine learning and quantum computing — with two complementary directions. ML for quantum — using neural networks and learning-based methods to accelerate quantum computation (e.g. circuit optimisation and compilation, error mitigation, calibration, variational-parameter optimisation, and surrogate modelling of quantum processes). …/with quantum — building and adapting classical AI methods, including neural networks and large language models, so they can benefit from quantum hardware and algorithms. You don't need to be a world expert in either quantum or LLMs — but you should be genuinely comfortable with neural networks and ...

replacement for clinical and statistical expertise, but as a powerful complement to it. We hold ourselves to measurable standards of improvement, and we build methods that can be evaluated, reproduced, and trusted in regulatory settings. You will work across the enterprise to define and deliver on AstraZeneca's most … development questions — leading cross-functional teamsspanning the keyBioPharmaceuticalsdisease areas of cardiovascular, renal, metabolic disease, respiratory,immunologyand cell-therapy.You and the team will develop reusable methods and enterprise-scale approaches that measurably advance the late-stage drug pipeline. This is a high-visibility opportunity to shape how AstraZeneca does ...

replacement for clinical and statistical expertise, but as a powerful complement to it. We hold ourselves to measurable standards of improvement, and we build methods that can be evaluated, reproduced, and trusted in regulatory settings. You will work across the enterprise to define and deliver on AstraZeneca's most … teams spanning the key BioPharmaceuticals disease areas of cardiovascular, renal, metabolic disease, respiratory, immunology and cell-therapy. You and the team will develop reusable methods and enterprise-scale approaches that measurably advance the late-stage drug pipeline. This is a high-visibility opportunity to shape how AstraZeneca does ...

senior specialist role focused on understanding the drivers of demand, outcomes, variation, inequalities and cost across our population. You will apply advanced analytical methods to complex system questions and translate findings into clear intelligence that supports senior decision making. The role will work closely with colleagues across analytics … generation and evaluation rather than routine reporting or dashboard development. Main duties of the job You will lead complex analytical and evaluative projects, applying methods such as regression, forecasting, segmentation, risk stratification, causal inference, impact evaluation, cost-effectiveness analysis and statistical modelling where appropriate. You will be experienced ...

continuously learn and improve Desirable: Study design and sample size experience Regulatory experience A basic understanding or willingness to learn Bayesian methods and causal inference modelling Experience within Oncology and/or CNS therapeutic areas Opportunities at Veramed Presenting at conferences and authoring publications Developing and ...

continuously learn and improve Desirable: Study design and sample size experience Regulatory experience A basic understanding or willingness to learn Bayesian methods and causal inference modelling Experience within Oncology and/or CNS therapeutic areas Opportunities at Veramed Presenting at conferences and authoring publications Developing and ...

Secondmind Labs is seeking a PhD-level researcher to advance probabilistic modelling, Bayesian optimization, and related fields. You will work in collaborative research teams to develop scalable algorithms addressing real-world problems for customers. You will publish papers, contribute to open-source libraries, and help shape ...

website). Key responsibilities Develop your own research program, with a view to developing new tools and techniques for probabilistic models, Bayesian optimization and related fields. Engage in team collaborations to meet research goals and report your research findings both internally and externally. Contribute to internal product … advantage: A background in decision making (Bayesian optimization, bandits, reinforcement learning, active learning) and/or probabilistic modelling and methods (Gaussian processes, Bayesian neural networks, Variational inference, etc). Experience in numerical programming (Python/NumPy/Tensorflow/PyTorch). Experience ...

research‐heavy MSc in Physics, Computer Science, Applied Mathematics or a related field. Familiarity with probabilistic algorithms, ideally also having implemented MCMC methods, Gibbs sampling, and energy‐based models. Familiarity with Bayesian inference, Causal AI, and the mathematical foundations of Boltzmann machines. Ability to review Python ...

multi-modal data ingestion and distribution modelling to production-grade visualisation dashboards. Must-have experience: Probabilistic Time Series Forecasting: Experience using probabilistic time series methods (eg, Bayesian, PyMC, Amazon DeepAR) on small or scarce datasets for distribution prediction. Data Lake & DBaaS Integration: Proven ability to build ...

knowledge sharing across the discipline. What we're looking forExperience as a Data Analyst (or similar role) within consumer-facing product teams Experience applying methods for statistical inference (e.g. Bayesian, Frequentist) and analysing A/B tests Experience in planning and completing analytical tasks on time ...

array of statistical genetics approaches (e.g., GWAS, colocalization, fine-mapping, Mendelian randomization, polygenic risk scores)Experience defining phenotypes from electronic health records.Knowledge of Bayesian inference, high dimensional statistics, causal inference.Experience with software engineering practices (version control [Git & GitHub], testing, documentation, agentic coding, containerisation)Experience … building and running reproducible pipelines e.g., using WDL, Snakemake, or NextFlow.Experience using cloud computing and trusted research environments e.g. DNAnexus or Verily Workbench.Experience with methods development within statistical geneticsYour PackageWe are committed to providing a transparent, supportive, and rewarding work environment.Compensation & GrowthCompetitive Salary: Salaries are externally benchmarked annually ...