26 to 50 of 131 Bayesian Methods Jobs in London

statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning, model cross validation, Bayesian inference, time-series analysis, simple NLP, effective SQL database querying, or using/writing simple APIs for models. We regard the ability to More ❯

statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning, model cross validation, Bayesian inference, time-series analysis, simple NLP, effective SQL database querying, or using/writing simple APIs for models. We regard the ability to More ❯

quantitative analyst or another relevant roleDegree in Applied Mathematics, Computer Science, Financial Engineering, Technology or EngineeringKnowledge of probability theory, inferential statistics, machine learning, Bayesian statistics, linear algebra, and numerical methodsExperience with statistical and machine learning models, such as regression-based models (e.g., logistic regression, linear regression, negative More ❯

at least three of the following areas: Generative AI, Supervised and Unsupervised Learning, Deep Learning, Computer Vision, Recommendation Systems, Knowledge Graphs, Probabilistic Inference, Bayesian Statistics, Reinforcement Learning. Strong industry experience, with a focus on taking concepts and models from conception to production and quantifying business impact. Proficiency More ❯

prediction in an energy or utilities setting. Experience in automating pricing and customer segmentation strategies. Familiarity with time-series forecasting, reinforcement learning, or Bayesian modelling. Research shows that some people are less likely to apply for a role unless they are 100% qualified. Your experience, skills and More ❯

Essential: Bachelor's or Master's degree in Statistics, Applied Mathematics, Econometrics, or related quantitative field 2-4 years of practical experience applying statistical methods to business problems Strong foundation in statistics, hypothesis testing, regression analysis, and experimental design Experience with marketing mix modeling, incrementality testing, or causal inference … a commercial setting Desired: Experience in the gambling/gaming sector or similar fast-paced digital business Knowledge of Bayesian statistical methods and probabilistic programming Familiarity with time series analysis and forecasting techniques Experience with A/B testing platforms and experimentation frameworks Understanding of data More ❯

Job Description Summary - Understands complex and critical business problems from various stakeholders and functions, formulates integrated analytical approaches to mine data sources, employs statistical methods and machine learning algorithms to solve unmet medical needs, discover actionable insights, and automate processes to reduce effort and time for repeated tasks. - Manages … related quantitative field (or equivalent). Over 6 years of experience in clinical drug development with extensive clinical trial exposure. Strong knowledge of statistical methods like time-to-event analysis, machine learning, meta-analysis, mixed-effect modeling, Bayesian methods, variable selection techniques (e.g., lasso, elastic More ❯

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 which provides a daily rate of £688 (Inside More ❯

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 which provides a daily rate of £688 (Inside More ❯

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 which provides a daily rate of £688 (Inside More ❯

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 which provides a daily rate of £688 (Inside More ❯

enable companies in the entertainment industry to significantly increase profit margins. You'll use a raft of different techniques from timeseries analysis to bayesian statistics, reinforcement learning & Monte Carlo Simulations. Your Experience : You'll likely come from a strong quantitative degree background in Science or Maths and More ❯

subtract mean and divide by standard deviation) Be familiar with all regression based options within Rova. For an MMM this includes GLS, CLS, Bayesian MMM and Hierarchical Bayesian. Create response curves and optimization spreadsheet or alternatively use available tools for budget allocation. This requires knowledge of internal More ❯

in machine learning and statistical modeling. Experience developing LSTM-based models for sequential forecasting or behavioral modeling. Familiarity with probabilistic graphical models (e.g., Bayesian networks, Markov random fields) for structured data relationships. Proficiency in programming languages such as Python or R, and experience with data manipulation and More ❯

Bonus: Experience implementing solutions in a production environment (e.g., continuous integration). Experience with Python. (Note: we mostly work in Python.) Experience with Bayesian statistics. The salary We expect to pay from £70,000 - £90,000 for this role. But, we're open-minded, so definitely include More ❯

statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning, model cross validation, Bayesian inference, time-series analysis, simple NLP, effective SQL database querying, or using/writing simple APIs for models. We regard the ability to More ❯

PyTorch). High mathematical competence and proficiency in statistics. Solid understanding of data science techniques such as supervised/unsupervised learning, cross-validation, Bayesian inference, time-series analysis, NLP, SQL, and API development. Ability to develop new algorithms is a fundamental skill. Leadership mindset focused on team More ❯

in causal inference, experimental design, and conformal prediction to join our innovative analytics data science team. In this role, you'll leverage advanced statistical methods to extract meaningful insights from complex data, design robust experiments, and develop predictive models with reliable uncertainty quantification. Core Responsibilities Design, implement, and analyse … causal inference experiments including natural experiments, and quasi-experimental methods Develop and apply conformal prediction frameworks to provide reliable uncertainty estimates for machine learning models Identify and control for confounding variables in observational studies Create robust statistical methodologies for causal effect estimation Collaborate with cross-functional teams to translate … terms Qualifications Advanced degree (MS or PhD) in a quantitative discipline with deep understanding of statistics 3+ years of professional experience in applying statistical methods to real data . Demonstrated expertise in experimental design, including randomized controlled trials and observational study methodologies Strong understanding of conformal prediction theory and More ❯

define projects. Knowledge of version control systems, particularly Git, with experience managing codebases and collaborating in team environments. Nice to haves: experience with Bayesian modelling and probabilistic programming libraries such as PyMC; proven ability in the creation and deployment of fraud detection models; familiarity with Amazon Sagemaker More ❯

statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning, model cross validation, Bayesian inference, time-series analysis, simple NLP, effective SQL database querying, or using/writing simple APIs for models. We regard the ability to More ❯

Ability to explain ideas and present results to non-technical audiences Strong stakeholder management skills Experience with marketing mix models and/or Bayesian time series would be a big plus Strong theoretical understanding and experience with key classification and regression models is a plus Please note More ❯

statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning, model cross validation, Bayesian inference, time-series analysis, simple NLP, effective SQL database querying, or using/writing simple APIs for models. We regard the ability to More ❯

statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning, model cross validation, Bayesian inference, time-series analysis, simple NLP, effective SQL database querying, or using/writing simple APIs for models. We regard the ability to More ❯

like to share. Skills, Knowledge and Expertise Technical skills Good understanding of linear algebra (vectors, matrices, tensors etc.) and statistics (probability distributions, probability, bayesian stats etc.) Good working knowledge of Python Implementing end-to-end ML pipelines Hand-on experience in Python data science ecosystem (e.g. numpy More ❯

probabilistic representations for diverse ML tasks. Build and optimize tools for scalable probabilistic inference under memory, latency, and compute constraints. Apply and innovate on methods like Bayesian neural networks , variational autoencoders , diffusion models , and Gaussian processes for modern AI use cases. Collaborate closely with product, engineering … uncertainty quantification, and responsible AI. Requirements/Qualifications: Strong experience in Bayesian inference and probabilistic modeling : PGMs, HMMs, GPs, MCMC, variational methods, EM algorithms, etc. Proficiency in Python (must) and familiarity with PyMC, NumPyro, TensorFlow Probability , or similar probabilistic programming tools. Hands-on experience with classical … ML and modern techniques, including deep learning , transformers , diffusion models , and ensemble methods . Solid understanding of feature engineering, dimensionality reduction, model construction, validation, and calibration. Experience with uncertainty quantification and performance estimation (e.g., cross-validation, bootstrapping, Bayesian credible intervals). Familiarity with database and data More ❯