1 to 25 of 61 Remote 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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

for the LGBTQ+ business community. Please do not contact the recruiter directly. Summary Understand complex and critical business problems, formulate integrated analytical approaches to mine data sources, employ statistical methods and machine learning algorithms to help solve unmet medical needs, discover actionable insights, and automate processes to reduce effort and time for repeated use. Manage the implementation and adherence … science, biostatistics, or a related quantitative field (or equivalent). Over 3 years of experience in clinical drug development with extensive exposure to clinical trials. Strong knowledge of statistical methods such as survival analysis, machine learning, meta-analysis, mixed-effects modeling, Bayesian methods, and variable selection techniques. Proficiency in R and Python, with experience in 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 ❯

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 ❯

don't tick all the boxes, you'll certainly learn on the job! Essential MSc or PhD, or equivalent experience in a quantitative field Deep theoretical knowledge of statistical methods and ML algorithms and their practical applications. Strong proficiency in SQL and Python, especially with core ML libraries (e.g. scikit-learn, XGBoost, SciPy, PyTorch) Extensive hands-on experience in … version control (e.g. Git), code reviews, CI/CD pipelines, unit testing, and collaborative workflows such as Agile. Experience with optimisation techniques (e.g. gradient descent, Bayesian methods, Lagrangian methods etc) and their practical application. Experience in UK General Insurance (Motor and/or Home), with a solid understanding of pricing foundations and regulatory governance. Familiarity 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 the definition, implementation, and adherence to the overall data lifecycle … data science, biostatistics, or a 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 net, random forest), and clinical 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 ❯