1 to 25 of 81 Bayesian Methods Jobs in the UK

understanding of statistical modelling/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 mathematical foundations, Have a good More ❯

and familiarity with R for MMM. In-depth understanding of statistical modelling and ML techniques. Experience with regression models in MMM context. Solid experience with Probabilistic Programming and Bayesian Methods. Proficiency in mining large, complex datasets using SQL and Spark. Strong knowledge of statistical techniques and mathematical foundations. Working knowledge of Pymc, cloud data science frameworks, and More ❯

opportunity to extend long-term. This is an exciting opportunity to join a high-performing Data Science team focused on advancing marketing effectiveness through advanced econometric modelling—including Bayesian Marketing Mix Modelling (MMM), Multi-Touch Attribution (MTA), and data-driven optimization strategies. Key Responsibilities Lead and manage data workflows: data extraction, transformation, validation, and exploratory analysis to … ensure modelling-readiness. Build and refine Bayesian MMM models that capture the drivers of key marketing and commercial KPIs. Use Python (and optionally R) to design, build, and improve base and advanced models—integrating prior knowledge, probabilistic reasoning, and real-world constraints. Develop and present ROI workbooks, response curves, and optimization frameworks for marketing budget allocation. Run … test models, identifying opportunities for improvement and ensuring robustness, interpretability, and business relevance. Requirements Extensive experience in building and deploying Marketing Mix Models, with a strong focus on Bayesian methods. Expert-level proficiency in Python, especially with pandas, NumPy, and probabilistic programming libraries such as PyMC. Experience with R is a bonus, particularly for MMM-related workflows. More ❯

equivalent experience with experience in research Experience in client delivery with direct client contact Proven experience applying machine learning techniques to solve business problems Proven experience in translating technical methods to non-technical stakeholders Strong programming experience in python (R, Python, C++ optional) and the relevant analytics libraries (e.g., pandas, numpy, matplotlib, scikit-learn, statsmodels, pymc, pytorch/tf …/keras, langchain) Experience with version control (GitHub) ML experience with causality, Bayesian statistics & optimization, survival analysis, design of experiments, longitudinal analysis, surrogate models, transformers, Knowledge Graphs, Agents, Graph NNs, Deep Learning, computer vision Ability to write production code and object-oriented programming More ❯

role Degree in Applied Mathematics, Computer Science, Financial Engineering, Technology or Engineering Knowledge of probability theory, inferential statistics, machine learning, Bayesian statistics, linear algebra, and numerical methods Experience with statistical and machine learning models, such as regression-based models (e.g., logistic regression, linear regression, negative binomial regression), tree-based models (e.g., random forests), support vector machines More ❯

libraries of predictive features and 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, and business teams to build … trends in generative modeling, causality, 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 processing tools (e.g., SQL, MongoDB, Spark, Pandas). Ability to translate ambiguous business problems into More ❯

stakeholders and translating research into practical solutions for predictive analytics. Experience in solution design, architecting and outlining data analytics pipelines and flows. Advanced Mathematics skills including experience with Bayesian statistics, linear algebra and MVT calculus, advanced data modelling and algorithm design experience. Design and deployment experience using Tensor Flow, Spark ML, CNTK, Torch or Caffe. The perks More ❯

outputs based on specific modelling, marketing, and business knowledge. Collaborate with subject matter experts and stakeholders to clarify business questions and enhance feature selection. Apply probability, frequentist vs Bayesian statistics, and statistical concepts like regression, ANOVA, AB testing. Select appropriate measurement techniques/designs to answer business questions and explain trade-offs. Define and build data models More ❯

core technologies Collaborate with data scientists and engineers to integrate models into pipelines and tools. Stay current with academic and industry advances in temporal modeling and deep learning. Document methods and support reproducibility, validation, and publication where appropriate. Essential Requirements Strong programming and modeling skills in Python (NumPy, PyTorch or TensorFlow, SciPy, pandas). In-depth knowledge of machine … learning for time-series, including RNNs, LSTMs, GRUs, transformers, attention mechanisms. Solid understanding of probabilistic models (HMMs, Bayesian inference, graphical models). Experience designing or adapting dynamic programming algorithms. A graduate degree (PhD or MSc) in Computer Science, Mathematics, Physics, Bioinformatics, or a related field Ability to work in a research-style setting and translate ideas into More ❯

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 which provides a daily rate of £688 (Inside IR35). In terms of More ❯

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 which provides a daily rate of £688 (Inside IR35). In terms of More ❯

Self-motivated, enthusiastic, and organised, with excellent attention to detail Proactive and delivers to timescales Desirable Attention to detail in creating documentation The ability to conduct descriptive statistics, Bayesian statistics, Inferential statistics, Probability distributions and theory, dimensionality reduction and sampling etc Strong numerical and statistical skills Additional Requirements Essential Willing to be flexible in working practices Improvement More ❯

teams to design, implement, and optimize data pipelines and predictive models that inform trading decisions and enhance operational efficiency. RESPONSIBILITIES: Primary Focus: Probabilistic Weather Modelling: Research and prototype probabilistic methods (Bayesian inference, state-space filtering, change-detection tests, etc.) that flag when fresh weather guidance materially diverges from prior outlooks. Continuous Development and Improvement: Calibrate confidence … of a degree in Statistics, Applied Maths, Physics or related field. Minimum of 2 years working in a Data Science related role Proven depth in probability & inference (e.g., Bayesian updating, time-series/state-space models, extreme-value theory). Hands-on Python for numerical analysis (numpy, pandas, xarray, SciPy/PyMC/PyTorch, or similar). More ❯

teams to design, implement, and optimize data pipelines and predictive models that inform trading decisions and enhance operational efficiency. RESPONSIBILITIES: Primary Focus: Probabilistic Weather Modelling: Research and prototype probabilistic methods (Bayesian inference, state-space filtering, change-detection tests, etc.) that flag when fresh weather guidance materially diverges from prior outlooks. Continuous Development and Improvement: Calibrate confidence … of a degree in Statistics, Applied Maths, Physics or related field. Minimum of 2 years working in a Data Science related role Proven depth in probability & inference (e.g., Bayesian updating, time-series/state-space models, extreme-value theory). Hands-on Python for numerical analysis (numpy, pandas, xarray, SciPy/PyMC/PyTorch, or similar). More ❯

Expertise in using data visualization tools such as Google Looker and working with Google's BigQuery. Experience in design and evaluation of A/B tests. Familiarity with Bayesian statistics, especially for hypothesis testing. Extra points for good knowledge of Bayesian programming (PyMC3, etc). Experience with application of optimization theory or reinforcement learning More ❯

mathematical competence and proficiency in 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 develop new algorithms when an More ❯

Understand complex and critical business problems, formulates integrated analytical approach to mine data sources, employ statistical methods and machine learning algorithms to contribute solving unmet medical needs, discover actionable insights and automate process for reducing effort and time for repeated use. To manage the implementation and adherence to the overall data lifecycle of enterprise data from data acquisition or … biostatistics, or other quantitative field (or equivalent). More than 3 years experience in clinical drug development with extensive exposure to clinical trials. Strong knowledge and understanding of statistical methods such as time to event analysis, machine learning, meta-analysis, mixed effect modeling, longitudinal modeling, Bayesian methods, variable selection methods (e.g., lasso, elastic net More ❯

sophisticated pricing algorithms which would 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 have worked 2-4 years More ❯

Who You Are - Proven track record leveraging machine learning to solve real-world problems; Expertise in one or more of the following: generative models, language models, computer vision, bayesian inference, causal reasoning & inference, transfer & multi-task learning, diffusion models, graph neural networks, active learning; Experience writing production-quality code with modern machine learning frameworks such as PyTorch … Experience in cell health and rejuvenation-related research area; Experience with identification and assessment of drug targets and/or therapeutic compounds; Experience in the application of machine learning methods to biological data, including genomics, transcriptomics, epigenetics, proteomics, and imaging; Track record in open-source software development, e.g., demonstrated by high-impact GitHub repository; Track record of high-caliber More ❯

Skills and Experience Strong academic background, with a degree in Computer Science (2:1 and above). Strong numerical background with a knowledge of key statistical principals e.g. Bayesian and frequentist statistics, probability distributions. Fundamental understanding of ML algorithms e.g. linear and logistic regression, random forest, neural networks, time series models. Experience with multiple programming languages, with More ❯

Skills and Experience Strong academic background, with a degree in Computer Science (2:1 and above). Strong numerical background with a knowledge of key statistical principals e.g. Bayesian and frequentist statistics, probability distributions. Fundamental understanding of ML algorithms e.g. linear and logistic regression, random forest, neural networks, time series models. Experience with multiple programming languages, with More ❯

Skills and Experience Strong academic background, with a degree in Computer Science (2:1 and above). Strong numerical background with a knowledge of key statistical principals e.g. Bayesian and frequentist statistics, probability distributions. Fundamental understanding of ML algorithms e.g. linear and logistic regression, random forest, neural networks, time series models. Experience with multiple programming languages, with More ❯

mathematical competence and proficiency in 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 develop new algorithms when an More ❯

magnetometry technology that will solve pressing challenges in aeronautical positioning and navigation, geophysical surveying, and magnetic anomaly detection. A key focus of the role will be developing and implementing methods for removing magnetic and/or vibrational noise induced by the platform on which the quantum sensor is mounted. The algorithms, signal processing, automation, and data analytics you develop … analysis and develop platform and environmental noise compensation techniques that enable high quality magnetic- and gravity-aided navigation using state-of-the-art digital signal processing and machine learning methods; Use data analytics to translate quantum sensor data streams into capabilities that solve pressing challenges for current and prospective end-users and customers; Work closely with other Research and … and/or platform motional characterisation and management; magnetic and/or vibrational signal denoising; quantum sensing and/or other sensing technologies. Knowledge and experience of machine learning methods to time series data including generative modelling. Contributing to a software system architecture with multiple components developed by different teams, adhering to best-practice software development precepts. Experience communicating More ❯

analytics or media environments. 💡 Bonus 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 Data Studio. A background in More ❯