1 to 25 of 75 Bayesian Methods Jobs in England

Science, Economics, Engineering etc.)Knowledge of advanced statistical concepts and techniques, e.g. skilled in linear algebra.Experience conducting hands-on analytics projects using advanced statistical methods such as generalized regression models, Bayesian methods, random forest, gradient boosting, neural networks, machine learning, clustering, or similar methodologies. Proficiency ...

related to consumer behaviour, market trends, and campaign performance. - Build, maintain, and apply statistical and predictive models, including regression, econometrics, Bayesian methods, and Marketing Mix Modelling (MMM). -Develop dashboards, charts, and- visualizations to communicate insights effectively to clients and internal teams. - Assist in developing marketing ...

Overview At EIT we are seeking an experienced and detail oriented Senior or Non Senior Scientist with recognised expertise in developing computational methods and algorithms for genomic sequencing data analysis. This is an exciting opportunity to develop novel computational approaches for microbial data that will become part … platform teams to deliver scientifically rigorous, scalable, and clinically relevant innovation in the microbial genomics space. Key Responsibilities Design, develop and evaluate novel computational methods in areas such as genome assembly, binning, and functional characterisation of genomes from metagenomic sequencing data. Collaborate with scientists across EIT to apply ...

least 3 data or ML products from problem definition to production deployment and monitoring.Demonstrated proficiency in clustering, predictive modeling, reinforcement learning, and Bayesian statistics.Hands-on experience in software engineering, MLOps, and deploying machine learning models at scale.Proficiency in SQL, Python, and familiarity with big data technologies (e.g. ...

from prototype through to operational deployment. Core Technical Skills Candidates should demonstrate strength in several of the following areas: Statistical modelling Probabilistic modelling Bayesian inference Forecasting Machine learning Simulation modelling Agent‐based modelling Optimisation techniques Experimental design Synthetic data generation Decision science Scenario analysis As important ...

other digital businesses.Experience with Snowflake, Power BI, Tableau or similar analytics tools.Exposure to attribution modelling, marketing mix modelling (MMM), Bayesian methods, or advanced experimentation frameworks.Work ShiftWestern Union values in-person collaboration, problem solving, and ideation whenever possible. We believe this fosters common ways of working and ...

digital businesses.* Experience with Snowflake, Power BI, Tableau or similar analytics tools.* Exposure to attribution modelling, marketing mix modelling (MMM), Bayesian methods, or advanced experimentation frameworks.**Work Shift**Western Union values in-person collaboration, problem solving, and ideation whenever possible. We believe this fosters common ways ...

cases as well as models. Train Machine Learning Models: Use machine learning and statistical modelling techniques such as recommendations, reinforcement learning, decision trees, Bayesian analysis, neural networks and transformers to develop and evaluate algorithms to address business use cases and/or to improve product/system ...

cases as well as models.Train Machine Learning Models: Use machine learning and statistical modelling techniques such as recommendations, reinforcement learning, decision trees, Bayesian analysis, neural networks and transformers to develop and evaluate algorithms to address business use cases and/or to improve product/system performance ...

Excel, and visualisation tools; experience with Databricks, Azure, or other cloud/data platforms is a plus. Strong understanding of econometrics, regression, Bayesian statistics, and advanced modelling techniques. Skilled in handling large datasets, optimising pipelines, and delivering high-quality outputs Track record of translating complex model results ...

Excel, and visualisation tools; experience with Databricks, Azure, or other cloud/data platforms is a plus.· Strong understanding of econometrics, regression, Bayesian statistics, and advanced modelling techniques.· Skilled in handling large datasets, optimising pipelines, and delivering high-quality outputs· Track record of translating complex model results ...

deep learning, neural networks, reinforcement learning, and transfer learning.Familiarity with traditional machine learning algorithms such as random forests, SVM, logistic regression, and Bayesian modelling for model building, validation, and testing.Familiarity with cloud platforms (e.g., Bedrock, AWS, Azure) for model deployment and the creation of production-ready pipelines.Proficiency ...

intellectually curious, analytical, and passionate about understanding cause-and-effect relationships in complex systems. You bring strong quantitative skills and enjoy applying rigorous methods to high-impact business challenges.Experience applying causal inference techniques in a commercial, product, marketing, or research environment.Strong understanding of causal inference methods. Exposure to experimental … such as Double Machine Learning, Causal Forests, Meta-Learners, and Uplift Models.Experience evaluating incrementality, or uplift using experimental data.Knowledge of Bayesian methods for causal analysis and decision-making.Experience in gaming, e-commerce, or subscription-based products.Experience working with large-scale data using PySpark or equivalent distributed ...

highly desirable.Strong knowledge of underlying mathematical foundations of statistics, exploratory analysis, pricing optimization and analytics (prescriptive and predictive), testing and modelling. Experience in Bayesian modelling is highly desirable.Proficiency in tools such as Google sheets (or Excel), SQL, Python and Tableau.Exceptional attention to detail and proactive mindset with ...

Excel, and visualisation tools; experience with Databricks, Azure, or other cloud/data platforms is a plus. Deep understanding of econometrics, regression, Bayesian statistics, and advanced modelling techniques. Proven ability to handle large datasets, optimise pipelines, and deliver high-quality outputs. Track record of translating complex model ...

Python, SQL, Excel, and visualisation tools; experience with Databricks, Azure, or other cloud/data platforms is a plus.Deep understanding of econometrics, regression, Bayesian statistics, and advanced modelling techniques.Proven ability to handle large datasets, optimise pipelines, and deliver high-quality outputs.Track record of translating complex model outputs ...

with strong foundations, deep structured‐data experience, and the judgement to apply the right level of complexity – from simple, robust baselines to more advanced methods where they add clear value. Key Responsibilities What will you be doing? Our direction of travel includes: Early-warning and lifecycle modelling. For rent … Time Time‐to‐event and state‐transition approaches (e.g. discrete‐time classification, roll‐rate models) for lifecycle and hazard problems. Forecasting using classical statistical methods, Bayesian/state‐space approaches, and modern pretrained time‐series foundation models. Connecting Models to Real Decisions Framing problems like contact ...

cases as well as models. Train Machine Learning Models: Use machine learning and statistical modelling techniques such as recommendations, reinforcement learning, decision trees, Bayesian analysis, neural networks and transformers to develop and evaluate algorithms to address business use cases and/or to improve product/system ...

Excel, and visualisation tools; experience with Databricks, Azure, or other cloud/data platforms is a plus. Deep understanding of econometrics, regression, Bayesian statistics, and advanced modelling techniques. Proven ability to handle large datasets, optimise pipelines, and deliver high-quality outputs. Track record of translating complex model ...

view on where it helps and where it doesn't.The requirementsEssential:Statistical foundations. Strong grounding in probability and statistics, with an understanding of Bayesian reasoning and experience applying hierarchical modelling or decision-making under uncertainty.Causal inference. Experience separating signal from noise in observational and experimental data.Business translation. ...

hypothesis-driven approachHave a practical understanding of machine learning, Deep Learning.Strong experience with Time series forecasting (ARIMA, ETS, Prophet, TS pattern detection, and ML methods) Strong experience with Causal inference, Intervention analysis and Scenarios simulation. Experience in developing probabilistic models, Proficient understanding of probability and statistics (esp. Bayesian methods)Application Details: Please take a copy of the Job Description, as this will not be available post closure of the advert. When applying for this role, please use the ‘cover letter’ of the online application or your CV to describe how you meet the competencies ...

production.Track record of promoting statistical rigour and experimental best practices in your prior roles.Prior experience using causal inference/econometric techniques and bayesian methodologies for hypothesis testing.Prior experience using large datasets with distributed computing (e.g. spark, hadoop or other map-reduce tech)Experience working in a fast ...

Challenge - Winners, multiple Best Paper Awards, and industry recognition. This role focuses on advancing the state-of-the-art in reinforcement learning (RL), Bayesian optimisation (BO), AI agents, large language models (LLMs), and vision-language models (VLMs). Whether your strength lies in fundamental algorithmic research ...

exposure to tools such as Meridian, Robyn, or PyMC Proficiency in Python and experience working with cloud platforms such as GCP Understanding of Bayesian statistics and marketing attribution concepts Ability to communicate technical findings clearly to non-technical audiences Interest in applying AI tools to enhance data ...

calculations, choosing the right unit of randomisation, guardrail metrics, interpreting inconclusive results.You have a proficient understanding of statistics — sample size, variance, multiple-comparisons, Bayesian reasoning.You know how to measure the incremental impact of ML models and CRM interventions end-to-end — defining success and guardrail metrics, designing ...