19 of 19 Permanent Bayesian Methods Jobs in the UK excluding London

Python skills; familiarity with R for MMM. Expertise in regression modeling, statistical and ML techniques . Experience with probabilistic programming, Bayesian methods, PyMC and MCMC. Proficient in SQL and/or Spark for large-scale data mining. Solid understanding of statistical foundations and mathematical modelling. Familiarity More ❯

ML techniques Experience with Regression based models applied to the context of MMM modelling Solid experience with Probabilistic Programming and Bayesian Methods Be an expert in mining large & very complex data sets using SQL and Spark Have in depth understanding of statistical modelling techniques and their More ❯

Points For: Experience working with large datasets or cloud platforms (e.g. GCP/Big Query). Exposure to advanced analytics approaches such as Bayesian or non-regression models. Familiarity with marketing measurement tools or platforms (e.g. Robyn, Meridian). Data viz skills in Power BI, Tableau, or More ❯

or consultancy environments. Bonus Points For: Familiarity with cloud platforms like GCP/BigQuery or handling large datasets. Exposure to Bayesian methods or advanced non-linear modelling approaches. Experience with open-source or proprietary MMM tools (e.g. Robyn, Meridian ). Strong data visualisation skills (Power BI More ❯

taking ownership of delivery Strong communication and problem-solving skills Desirable: Experience in demand forecasting or operations research Familiarity with Bayesian methods, Monte Carlo simulations, or reinforcement learning Exposure to high-scale SaaS or B2C products Why Apply? Build forecasting systems powering global commercial strategies Collaborate More ❯

field of machine learning. You will be apart of a highly unique research group across Natural Language Processing, Large Learning Models, deep learning, Bayesian & Signal Optimization with the exciting transition to the world of financial predication through the prism of machine learning. The most exceptional team members More ❯

models. Job Requirements: 2+ years' experience (ideally in a syndicate) developing sports betting models from scratch, in any sport Strong fundamental understanding of Bayesian statistics, variational inference, filtering techniques, state space and hierarchical models MSc+ level degree from well respected university in any STEM subject Excellent proficiency More ❯

models. Job Requirements: 4+ years' experience (ideally in a syndicate) developing sports betting models from scratch, in any sport Strong fundamental understanding of Bayesian statistics, variational inference, filtering techniques, state space and hierarchical models Excellent proficiency in any programming language, but with a preference for Python or More ❯

roles. Contributions to open-source projects in computational biology. Familiarity with advanced ML techniques such as Transformer architectures, Structured State-Space Models, or Bayesian Optimization. Track record of scientific publications in machine learning or computational biology. Experience in therapeutic or diagnostic development program More ❯

in top journals/conferences. Experience leading research projects or mentoring junior scientists (for senior roles). Background in generative models (e.g., diffusion, Bayesian Flow Networks) or multimodal ML Interest or experience in biological domains like drug discovery, genomics, proteomics, protein design etc. Responsibilities: Drive cutting-edge More ❯

day duties and responsibilities will include, but won’t be limited to building and improving sports prediction models To research recent advances in Bayesian updating to better extract signals from noisy data To design and build your own models using your own ideas To work closely with More ❯

external companies, academia, and internal scientific and data science teams Extensive hands-on coding and actively contribute to platform development Clearly articulate highly technical methods and results to diverse audiences and partners to drive decision-making findings through peer-reviewed publications and/or scientific conferences Qualifications Ph.D. in … Proficiency in one or more AI frameworks such as PyTorch or Tensorflow Extensive experience with one or more AI/ML fields including Bayesian Optimization/Bayesian Deep Learning/Probabilistic Machine Learning, Causal Inference and discovery Demonstrated proficiency in working with Foundation Models More ❯

sustain the pace to keep on growing? Will you make an impact with your desire to win? Role Overview: The Data Scientist - Commercial (Bayesian Focus) supports the Lead Data Scientist (Commercial) in delivering AI explorations identified via the AI Incubator process. This hands-on role requires expertise … in Bayesian techniques and commercial data science models, such as market mix modelling, to drive impactful business outcomes. Key Responsibilities: Develop and implement commercial data science solutions using Bayesian methodologies. Collaborate with business stakeholders to align AI initiatives with commercial objectives. Build and optimize … and similar solutions to inform decision-making. Provide insights and recommendations based on statistical analysis and machine learning. Stay abreast of advancements in Bayesian modelling and its commercial applications. Qualifications: PhD or Master's degree in Machine Learning, Data Science, or a related field. Strong expertise in More ❯

sustain the pace to keep on growing? Will you make an impact with your desire to win? Role Overview: The Data Scientist - Commercial (Bayesian Focus) supports the Lead Data Scientist (Commercial) in delivering AI explorations identified via the AI Incubator process. This hands-on role requires expertise … in Bayesian techniques and commercial data science models, such as market mix modelling, to drive impactful business outcomes. Key Responsibilities: · Develop and implement commercial data science solutions using Bayesian methodologies. · Collaborate with business stakeholders to align AI initiatives with commercial objectives. · Build and optimize … and similar solutions to inform decision-making. · Provide insights and recommendations based on statistical analysis and machine learning. · Stay abreast of advancements in Bayesian modelling and its commercial applications. Qualifications: · PhD or Master’s degree in Machine Learning, Data Science, or a related field. · Strong expertise in More ❯

specialist systems, game theory, decision support systems, multi-agent systems Data fusion and state estimation/tracking algorithms e.g. Kalman Filtering, multiple-model tracking methods, particle filters, grid-based estimation methods, Multi-Object-Multi-Sensor Fusion, data-association, random finite sets, Bayesian belief networks, Dempster … Shafer theory of evidence Machine Learning for regression and pattern recognition/discovery problems e.g. Gaussian processes, latent variable methods, support vector machines, probabilistic/statistical models, neural networks, Bayesian inference, random-forests, novelty detection, clustering Deep Learning e.g. Deep reinforcement learning, Monte-Carlo tree search More ❯

open-ended positions. Applicants are invited from any area of applied statistics, including statistical or actuarial data science. Those working in actuarial science, Bayesian statistics, statistical learning and/or actuarial statistics are particularly welcome to apply. Candidates who would like to work with the University's … field. You will have a strong track record of research in applied statistics, actuarial or statistical data science which includes fields such as Bayesian statistics, statistical learning, actuarial statistics, computational statistics and statistical methodology. You will either be established or have the potential to establish yourself as More ❯

systems tailored for biomedical discovery Contribute to long-term research programs focused on autonomous AI agents Lead and implement state-of-the-art ML methods into a therapeutic platform Publish high-impact research and support the development of patents Your Experience PhD/MSc (or equivalent) in Machine Learning … LLMs, generative AI, optimization, or sequential learning Experience with deep learning frameworks (TensorFlow, PyTorch, JAX, etc.) Expertise in one or more: reinforcement learning, Bayesian optimization, optimal experimental design, agents, or causality Experience in drug/target discovery, treatment modeling, or medical AI research Publications in high-impact More ❯

Grenoble IDEX. Application Instructions Interested applicants should write to us with: a letter of interest, CV, and should require two recommendation letters. Context Bayesian deep learning brings together two of the most important machine learning paradigms: Bayesian inference and deep learning. On the one … hand, Bayesian learning provides a theoretically sound framework to formalise the estimation of the architecture and the parameters of deep neural network models. On the other hand, deep learning offers new tools in Bayesian modelling, e.g. to learn flexible nonparametric priors or computationally efficient … few. While very effective, these models are computationally costly and require large quantities of data for their many parameters to be accurately estimated. Bayesian statistics offers a theoretically well-grounded framework to reason about uncertainty, and it is one of the cornerstones of modern machine learning. At More ❯

talented Quantitative Analyst to join their London based team. The successful candidate will use the extensive datasets to enhance existing predictive models, research new methods, and turn your insights into production-ready solutions. This research will involve a mix of well-executed analyses and innovative modelling to solve unique … challenges in football analytics, where traditional methods often need to be adapted or reinvented. To achieve this, you will have the freedom to explore and develop your own ideas while working collaboratively with a team of quants, developers, and analysts, to combine technical expertise with football knowledge. Key Requirements … predictive modelling, machine learning, and probability theory, preferably in sports or gaming/betting industries Familiarity with techniques such as Monte Carlo simulation, Bayesian modelling, mixed effects models, Kalman filters, GLMs, and time series forecasting. While expertise in every area isn’t expected, you should have a More ❯