1 to 25 of 159 Bayesian Methods Jobs in London

or a related technical or quantitative field, or equivalent practical experience. Nice to have: Experience with advanced experimentation methodologies (e.g., Bayesian methods, multi-armed bandits, causal inference). Experience specifically building or managing platforms for internal developers/users. Proven track record of successfully scaling experimentation … us? If you have one, can you share your personal website? Do you have experience with advanced experimentation methodologies (e.g., Bayesian methods, multi-armed bandits, causal inference)? * Select... Do you have experience working on creation of internal testing tooling? If so, can you briefly share what More ❯

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 … 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 More ❯

Python skills; familiarity with R for MMM. Expertise in regression modeling, statistical and ML techniques . Experience with probabilistic programming, Bayesian methods, PyMC and MCMC. Proficient in SQL and/or Spark for large-scale data mining. Solid understanding of statistical foundations and mathematical modelling. Familiarity 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 … 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 More ❯

Python skills; familiarity with R for MMM. Expertise in regression modeling, statistical and ML techniques . Experience with probabilistic programming, Bayesian methods, PyMC and MCMC. Proficient in SQL and/or Spark for large-scale data mining. Solid understanding of statistical foundations and mathematical modelling. Familiarity More ❯

Python skills; familiarity with R for MMM. Expertise in regression modeling, statistical and ML techniques . Experience with probabilistic programming, Bayesian methods, PyMC and MCMC. Proficient in SQL and/or Spark for large-scale data mining. Solid understanding of statistical foundations and mathematical modelling. Familiarity More ❯

Python skills; familiarity with R for MMM. Expertise in regression modeling, statistical and ML techniques . Experience with probabilistic programming, Bayesian methods, PyMC and MCMC. Proficient in SQL and/or Spark for large-scale data mining. Solid understanding of statistical foundations and mathematical modelling. Familiarity More ❯

Python skills; familiarity with R for MMM. Expertise in regression modeling, statistical and ML techniques . Experience with probabilistic programming, Bayesian methods, PyMC and MCMC. Proficient in SQL and/or Spark for large-scale data mining. Solid understanding of statistical foundations and mathematical modelling. Familiarity More ❯

Understands complex and critical business problems from a variety of stakeholders and business functions, formulate 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. … or equivalent). More than 6 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 More ❯

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 More ❯

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 More ❯

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 More ❯

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 More ❯

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 More ❯

models. Deep understanding of statistical modeling and ML techniques. Experience with regression-based models in MMM contexts. Solid experience with probabilistic programming and Bayesian methods. Expertise in mining large, complex datasets using SQL and Spark. Understanding of statistical modeling techniques and their mathematical foundations. Working knowledge of More ❯

Develop new ML models and AI techniques Lead on research projects within a global team Review relevant AI/ML literature to identify emerging methods, technologies, and best practices Explore new data sources and discover techniques for best leveraging data Minimum Qualifications A Master's or PhD in a … background applying Deep Learning techniques (including implementing custom architectures, optimizing model performance, developing novel loss functions, and deploying production-ready solutions) Familiarity in statistical methods for Machine Learning (e.g. Bayesian methods, HMMs, graphical models, dimension reduction, clustering, classification, regression techniques, etc.) Familiarity with PyTorch, TensorFlow More ❯

Develop new ML models and AI techniques Lead on research projects within a global team Review relevant AI/ML literature to identify emerging methods, technologies, and best practices Work towards long-term research goals, while identifying intermediate milestones Build relationships and collaborate with academics and institutions Explore new … research experience, or 5 or greater years of work experience (actual job title/position will be commensurate to experience) Good background in statistical methods for Machine Learning (e.g. Bayesian methods, HMMs, graphical models, dimension reduction, clustering, classification, regression techniques, etc.) Familiarity with Deep Learning … abilities in Python and/or C++ Preferred Qualifications: 2D & 3D Generative AI Reinforcement Learning LLMs and Natural Language Processing Computational geometry and geometric methods (e.g. shape analysis, topology, differential geometry , discrete geometry , functional mapping, geometric deep learning, graph neural networks) Multi-modal deep learning and/or information More ❯

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 More ❯

Develop new ML models and AI techniques Lead on research projects within a global team Review relevant AI/ML literature to identify emerging methods, technologies, and best practices Explore new data sources and discover techniques for best leveraging data Minimum Qualifications A Master's or PhD in a … background applying Deep Learning techniques (including implementing custom architectures, optimizing model performance, developing novel loss functions, and deploying production-ready solutions) Familiarity in statistical methods for Machine Learning (e.g. Bayesian methods, HMMs, graphical models, dimension reduction, clustering, classification, regression techniques, etc.) Familiarity with PyTorch, TensorFlow More ❯

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 … 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 More ❯

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 … 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 More ❯

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 … 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 More ❯

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 … 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 More ❯

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 … 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 More ❯

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 More ❯