1 to 25 of 138 Bayesian Methods Jobs in England

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

The successful candidate will play a key role in developing and implementing advanced pricing and marketing optimization models, leveraging expertise in Bayesian statistics, causal inference, econometric methods, and proficiency in Python to deliver impactful insights in the CPG domain. Responsibilities Design and build sophisticated pricing and marketing optimization models using Bayesian, causal inference … Computer Science, Statistics, Physics, Mathematics) Minimum 5 years of experience in data science, focusing on pricing and marketing optimization Expertise in Bayesian, causal inference, and econometric methods Strong proficiency in Python and experience deploying models Experience with cloud platforms, preferably Azure, Databricks, MLFlow, Airflow, and Plotly Dash Nice to Have PhD in a relevant field Experience 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 ❯

science, with a strong foundation 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 visualization tools. Demonstrated ability to 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 ❯

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 ❯