1 to 25 of 171 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 ❯

Initial 12 month contract + potential to extend long-term due to 3/4 year project scope. Start Date: ASAP We have an excellent opportunity for a Bayesian Data Scientist to join a leading FMCG Brand, Initial 12 month contract + 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 … 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 ❯

Initial 12 month contract + potential to extend long-term due to 3/4 year project scope. Start Date: ASAP We have an excellent opportunity for a Bayesian Data Scientist to join a leading FMCG Brand, Initial 12 month contract + 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 … 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 ❯

Initial 12 month contract + potential to extend long-term due to 3/4 year project scope. Start Date: ASAP We have an excellent opportunity for a Bayesian Data Scientist to join a leading FMCG Brand, Initial 12 month contract + 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 … 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 ❯

Social network you want to login/join with: Bayesian Data Scientist, london (city of london) col-narrow-left Client: ECM Talent Location: london (city of london), United Kingdom Job Category: Other - EU work permit required: Yes col-narrow-right Job Views: 4 Posted: 26.06.2025 Expiry Date: 10.08.2025 col-wide Job Description: Bayesian Data … Initial 12 month contract + potential to extend long-term due to 3/4 year project scope. Start Date: ASAP We have an excellent opportunity for a Bayesian Data Scientist to join a leading FMCG Brand, Initial 12 month contract + 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 More ❯

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 ❯

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 ❯

expertise to enhance the analytic capabilities of the Safety & Surveillance group. Activities include supporting the group with epidemiological expertise and assessments, identifying and addressing data gaps, and developing novel methods for pharmacovigilance. The division also engages in research, often in collaboration with external partners and academic institutions, regularly publishing papers in the scientific literature. This role supports the Agency … visualisation methods. Work cross-functionally to enable the delivery of innovative evidence-generation and collaborate with external partners to enable data-driven discovery in medical product safety. Apply analytical methods to patient data collated through vigilance systems to support assessment of the safety of medicines and medical devices and advise on the refinement of AI tools for data processing. … expertise to enhance the analytic capabilities of the Safety & Surveillance group. Activities include supporting the group with epidemiological expertise and assessments, identifying and addressing data gaps, and developing novel methods for pharmacovigilance. The division also engages in research, often in collaboration with external partners and academic institutions, regularly publishing papers in the scientific literature. This role supports the Agency More ❯

mathematical foundations of Machine Learning algorithms Proficient in Python Have a deep knowledge of a sufficiently broad area of technical specialism (e.g. Time Series, Combinatorial Optimisation, Reinforcement Learning, Bayesian Statistics, NLP etc.) Good knowledge in mining large & complex data sets using SQL and Spark. Preferred Will be curious, enjoy problem solving and have empathy for the problems More ❯

Data Science and Time Series analysis Have in depth understanding of statistical modelling/ML techniques for time series forecasting (ARIMA, ETS, Prophet, Time Series pattern detection, and ML methods) Experience with Causal inference, Intervention analysis, Counterfactuals Estimation, Optimization and Scenarios simulation Good experience with Probabilistic Programming and Bayesian Methods Experience using machine learning frameworks 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 ❯

team at Sojern develops services and pipelines to optimize advertising campaign performance and applies machine learning at scale for thousands of customers. We utilize a range of algorithms and methods to do this including: Bayesian statistics and supervised learning to model conversion rates and revenue Scaled AI/ML with Tensorflow/Vertex AI to model More ❯

as a data scientist, quantitative researcher, or similar 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 models like regression, tree-based models, support vector machines, PCA, clustering, matrix factorization, deep learning, etc. Proficiency in Python and 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 ❯

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 ❯

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 ❯

mathematical foundations of Machine Learning algorithms Proficient in Python Have a deep knowledge of a sufficiently broad area of technical specialism (e.g. Time Series, Combinatorial Optimisation, Reinforcement Learning, Bayesian Statistics, NLP etc.) Good knowledge in mining large & complex data sets using SQL and Spark. Preferred Will be curious, enjoy problem solving and have empathy for the problems 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 ❯

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 ❯

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 ❯

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 ❯

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 ❯