1 to 25 of 51 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 ...

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 ...

priors) into calibrated probability estimates. Build pricing and edge frameworks: fair value, uncertainty bands, expected value, and model drift/regime diagnostics. Design evaluation methods (proper scoring rules like log loss/Brier score, calibration curves, back-tests with realistic costs and constraints). Trading & Market Design (Applied) Identify … with SQL; experience with streaming systems (Kafka), workflow orchestration (Airflow), and cloud (AWS/GCP/Azure). Experience with Bayesian methods, probabilistic programming (Stan/PyMC), or ensemble methods. Familiarity with rigorous experimentation: online/offline evaluation, data leakage prevention, and model governance. Tech Stack ...

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 ...

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 ...

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 ...

with other research teams in Alphabet. AI Foundations areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation. In this hybrid role, you will report to a Senior Staff Software Engineer. You will: Partner with ...

required to obtain UK Security Clearance. Desirable Experience Experience with any of the following would be beneficial: Statistical modelling, Bayesian methods, uncertainty estimation, or probabilistic techniques. Synthetic data generation, multi-modal AI, or action recognition. Machine learning development tools, experiment tracking, dataset management, or CI/ ...

with time‐series modelling and industrial or IoT data. You have experience in any of: dynamical systems, reinforcement learning, system identification, optimisation or Bayesian statistics. You are used to working in a fast‐paced startup environment with an agile process. You have a degree in machine learning ...

Analyst or Data Scientist Minimum 2:1 in a Quantitative subject ideally Masters Experience OR an interest in Modelling – CHAID, Cluster Analysis, Bayesian Algorithms, NN, Experience with visualisation tools would be nice Python and SQL experience ideally - Python a MUST Experience in a Data Science role with ...

markets (e.g. UK and Germany). Regulatory submission experience in key markets (e.g. EU and USA). Application of indirect comparison methodology (e.g. Bayesian/Frequentist NMA and MAIC/STC methodologies). Experience of performing adjustment for treatment switching/cross‐over within clinical trials (e.g. ...

years line management responsibilities.Hands-on experience in population PK/PD, disease progression modeling, and clinical trial simulations.Strong understanding and knowledge of Bayesian methodologies, model-based meta-analyses, PBPK, QSP, and AI/ML in Pharmacometrics.Expertise in R, NONMEM or related pharmacometrics software.Are you ready ...