Strong understanding of A/B testing and test statistics Highly numerate and data-driven Experience analysing customer trends and behaviours Good understanding of frequentist and/or Bayesian analysis methodologies Additional Information Benefits package includes: Great compensation package and discretionary bonus Core benefits include pension, bupa healthcare, sharesave scheme and more 25 days annual leave with More ❯
South West London, London, United Kingdom Hybrid / WFH Options
Experian Ltd
testing and test statistics Highly numerate and data-driven Excel skills (can maintain complex spreadsheets) Experience analysing customer trends and behaviours Good understanding of frequentist and/or Bayesian analysis methodologies Additional Information Benefits package includes: Hybrid working Great compensation package and discretionary bonus Core benefits include pension, bupa healthcare, sharesave scheme and more 25 days annual More ❯
General Theory for Big Bayes" and Grenoble IDEX. 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 … 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 Bayesianmodelling, e.g. to learn flexible nonparametric priors or computationally efficient posterior distribution approximations. State of the art The field of machine learning has recently been much impacted by … processing, to cite just a few. While very effective, these models are computationally costly and require large quantities of data for their many parameters to be accurately estimated. Bayesianstatistics offers a theoretically well-grounded framework to reason about uncertainty, and it is one of the cornerstones of modern machine learning. At the same time, the theory More ❯
years of experience in predictive modelling, machine learning, and probability theory, preferably in the sports or gaming/betting industries. Familiarity with techniques such as Monte Carlo simulation, Bayesianmodelling, mixed effects models, Kalman filters, GLMs, and time series forecasting. Strong programming skills, particularly in Python. Experience in exploring new datasets, identifying data quality issues, and handling More ❯
City of London, London, United Kingdom Hybrid / WFH Options
Harrington Starr
years of experience in predictive modelling, machine learning, and probability theory, preferably in the sports or gaming/betting industries. Familiarity with techniques such as Monte Carlo simulation, Bayesianmodelling, mixed effects models, Kalman filters, GLMs, and time series forecasting. Strong programming skills, particularly in Python. Experience in exploring new datasets, identifying data quality issues, and handling More ❯
developed front-end componentry Interested in shaping the backend Python API or becoming involved in full stack development. Desired/Obtained Qualifications and experience: Some general knowledge of Bayesiannetworks or probability would be helpful Experience working closely with data scientists and other stakeholders, not just developers, and be comfortable demoing their work The product will be More ❯
London, England, United Kingdom Hybrid / WFH Options
ZipRecruiter
of impactful research with publications 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 research in multimodal generative models More ❯
and Duties Your day-to-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 expert traders and developers to More ❯
forecasting, optimization, recommender systems, causal inference, or Bayesian methods. Proficiency in programming languages used in machine learning and familiarity with common frameworks. Solid grasp of statistical methodsand software development best practices. Ability to work independently, manage timelines, and deliver prototypes or models aligned with business needs. Strong collaboration skills and comfort working across technical andMore ❯
London, England, United Kingdom Hybrid / WFH Options
Matterhorn
Machine Learning Scientist (General Interest) Date: Aug 1, 2024 We are looking for a Machine Learning Scientist in Bayesian Optimisation. You will be working closely with scientists in the materials space to build machine learning models (OptApps) that will inform laboratory experimentation schedules, anywhere from complete-manual to fully … self-driving. Responsibilities: Researching the right Bayesian Optimisation techniques for a variety of experimentation challenges: multi-fidelity, multi-source, multi-step (generally known as ‘grey-box’ methods). Implementing these models for partners in the pharmaceuticals industry, focusing on ease of usability and interpretation. Validating the effectiveness of the models and tackling deeper research challenges, with … the opportunity to publish. Minimum Requirements: You must have relevant experience in Machine Learning, specifically Bayesian Optimisation, at MSc/PhD level to complete the above work. Location: Hybrid/Oxford/London (anywhere in the UK) Matterhorn Studio is leading a paradigm shift towards peer-reviewed plug-in Bayesian Optimisation. We’re looking More ❯
experiment analysis techniques, including Bayesianand Causal Inference. Modeling Skills: Experience working on advanced modelling concepts. SQL and Statistical Analysis: Strong proficiency in SQL and statistical methods for analyzing complex data sets. Proven experience in conducting impact analysis to assess viable opportunities and scoping their relevance. Track Record: A successful track record of managing complex projects More ❯
you want to login/join with: The Quant team at Mustard Systems create predictive models that are globally recognised as best-in-class. We use cutting-edge statistical methods to model a wide variety of sports, often developing our own techniques. We're looking for talented individuals to join our team. You can expect: To research recent advances … in Bayesian updating to better extract signals from noisy data To join a close-knit, collaborative team of mathematicians and enjoy a high level of responsibility from an early stage To design and build your own models using your own ideas To work closely with expert traders and developers to improve your models Requirements What we're More ❯
London, England, United Kingdom Hybrid / WFH Options
ZipRecruiter
continuous learning to keep the team ahead of the curve. Contribute to a collaborative, inclusive culture that encourages innovation and growth. Service & Capability Development Continuously improve our CRQ delivery methods, toolkits, and playbooks. Feed ideas into product development – helping shape new features and use cases. Contribute to embedding CRQ into broader risk domains like operational resilience, risk appetite, and … professional services environment, balancing quality, scope, time, and budget. Skills we’d love to see/Amazing Extras: Hands-on experience delivering CRQ services or using structured risk quantification methods (e.g. Monte Carlo simulation, FAIR, Bayesian models). Familiarity with CRQ tools and platforms (ours or others in the market). Experience embedding CRQ into wider More ❯