1 to 25 of 30 Bayesian Methods Jobs in England

in the AI Lab. Responsibilities 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 field related to AI/… disciplines Strong background applying Deep Learning techniques (including implementing custom architectures, optimizing model performance, developing novel loss functions, metrics, and benchmarks, 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, PyTorch Lightning, or similar frameworks Strong 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 ❯

behaviour, and come up with recommendations. Data Scientists do this by listening to team members, understanding context and challenging business ideas. Data Scientists use diverse techniques - frequentist and Bayesian statistics, machine learning, exploratory and explanatory data analysis, causal inference, data visualization, monte carlo modelling, econometric analysis, etc. Such broad requirements call for the ability to learn quickly More ❯

sophisticated pricing algorithms which would enable companies in the entertainment industry to significantly increase profit margins. You'll use a raft of different techniques from timeseries analysis to bayesian statistics, reinforcement learning & Monte Carlo Simulations. Your Experience : You'll likely come from a strong quantitative degree background in Science or Maths and have worked 2-4 years More ❯

and externally. Continuously develop your own AI/ML and engineering skills. Essential skills and experience Academic or industrial research experience in machine learning, especially probabilistic models and Bayesian statistics. Proficiency in designing and programming advanced ML algorithms. Ability to apply state-of-the-art research to real-world problems. Proficiency in operating and evolving advanced ML More ❯

and externally. Continuously develop your own AI/ML and engineering skills. Essential skills and experience Academic or industrial research experience in machine learning, especially probabilistic models and Bayesian statistics. Proficiency in designing and programming advanced ML algorithms. Ability to apply state-of-the-art research to real-world problems. Proficiency in operating and evolving advanced ML More ❯

level Python programming (NumPy, PyTorch/TensorFlow, SciPy, pandas) Deep expertise in time-series and sequence modelling (RNNs, LSTMs, transformers, attention mechanisms) Strong foundation in probabilistic models (HMMs, Bayesian inference, graphical models) Track record of delivering impactful research or production systems What's in it for me? Salary of £80,000 - £100,000 dependent on experience Work More ❯

level Python programming (NumPy, PyTorch/TensorFlow, SciPy, pandas) Deep expertise in time-series and sequence modelling (RNNs, LSTMs, transformers, attention mechanisms) Strong foundation in probabilistic models (HMMs, Bayesian inference, graphical models) Track record of delivering impactful research or production systems What's in it for me? Salary of £80,000 - £100,000 dependent on experience Work More ❯

solver with a desire to learn Organised and self-motivated Desired Skills Master’s degree in machine learning, mathematics or statistics Understanding of probabilistic model development Experience of Bayesian modelling Good understanding of software design principles and best practices Good knowledge of at least one object-oriented language Familiarity with cloud platforms (e.g., Azure, AWS, GCP) and More ❯

solver with a desire to learn Organised and self-motivated Desired Skills Master’s degree in machine learning, mathematics or statistics Understanding of probabilistic model development Experience of Bayesian modelling Good understanding of software design principles and best practices Good knowledge of at least one object-oriented language Familiarity with cloud platforms (e.g., Azure, AWS, GCP) and More ❯

and data teams to ensure scalable solutions Requirements Extensive experience in data science, including applied statistics and machine learning Familiarity with supervised and unsupervised learning, NLP, neural networks, Bayesian statistics, time-series forecasting, collaborative filtering, and deep learning Proficiency in Python and ML libraries (e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) Experience with cloud platforms (GCP More ❯

experience within predictive modelling, machine learning, and probability theory. Ideally this would be within sports or gaming/betting industries. Understanding of techniques such as Monte Carlo simulation, Bayesian modelling, GLMs, mixed effects models, time series forecasting etc Strong programming ability, preferably in Python SQL and relational databases The company offer some great benefits including a bonus More ❯

CI/CD practices and model governance. Essential Skills Master’s degree in Machine Learning, Mathematics, or Statistics . Strong understanding of probabilistic model development . Experience with Bayesian modelling . Solid grasp of software design principles and best practices . Proficiency in at least one object-oriented programming language . Familiarity with cloud platforms (Azure, AWS More ❯

tracking, positiona l), and libraries like mplsoccer Advanced MLOps & Modelling : Experience with the Vertex AI ecosystem, especially pipelines, and advanced techniques such as player valuation , tactical modelling , etc. Bayesian Modelling : Knowledge of probabilistic programming (e.g., PyMC ) for uncertainty-aware predictions Stakeholder Collaboration : Demonstrated ability to work directly with stakeholders to scope, iterate, and deliver impactful solutions in More ❯

tracking, positiona l), and libraries like mplsoccer Advanced MLOps & Modelling : Experience with the Vertex AI ecosystem, especially pipelines, and advanced techniques such as player valuation , tactical modelling , etc. Bayesian Modelling : Knowledge of probabilistic programming (e.g., PyMC ) for uncertainty-aware predictions Stakeholder Collaboration : Demonstrated ability to work directly with stakeholders to scope, iterate, and deliver impactful solutions in More ❯

libraries like mplsoccer . Advanced MLOps & Modelling: Deeper experience with the Vertex AI lifecycle (especially Pipelines ) and advanced modelling techniques relevant to football (player valuation, tactical analysis). Bayesian Modelling: Experience with probabilistic programming (e.g., PyMC). Stakeholder Management: Proven success working directly with business stakeholders to define and deliver impactful solutions. What They Offer Work that More ❯

junior team members, leading research projects, and guiding collaborative efforts is a plus. Knowledge of the following machine learning domains is a plus. Generative models leveraging diffusion or Bayesian Flow Networks. Large-scale distributed machine learning training. Knowledge, experience or interest in the following biological domains is a plus. Drug discovery and protein engineering. Understanding of protein More ❯

skills Track record shipping ML products PhD or other experience in a research environment Deep experience in an applicable ML area. E.g. NLP, Deep learning, Bayesian methods, Reinforcement learning, clustering Strong stats or math background We are a well treated bunch, with awesome benefits! If there's something important to you that's not on this More ❯

including credit, collections, and data engineering—to translate business objectives into robust analytical solutions. Build software using modern technology to enable investing and asset management at scale. Apply Bayesian modelling and probabilistic programming techniques to address uncertainty and improve prediction accuracy. Analyse large-scale datasets to identify key drivers, trends, and early warning signals within NPL portfolios. … integrating new techniques and tools. Requirements: University degree in a STEM field (e.g., Mathematics, Statistics, Computer Science, Engineering, Physics, Economics); advanced degree preferred. Strong expertise in statistical modelling, Bayesian inference, and machine learning. Proficient in Python (using libraries such as NumPy, pandas, scikit-learn, PyMC or Stan) Experienced in SQL. Ability to write efficient and robust queries. More ❯

including credit, collections, and data engineering—to translate business objectives into robust analytical solutions. Build software using modern technology to enable investing and asset management at scale. Apply Bayesian modelling and probabilistic programming techniques to address uncertainty and improve prediction accuracy. Analyse large-scale datasets to identify key drivers, trends, and early warning signals within NPL portfolios. … integrating new techniques and tools. Requirements: University degree in a STEM field (e.g., Mathematics, Statistics, Computer Science, Engineering, Physics, Economics); advanced degree preferred. Strong expertise in statistical modelling, Bayesian inference, and machine learning. Proficient in Python (using libraries such as NumPy, pandas, scikit-learn, PyMC or Stan) Experienced in SQL. Ability to write efficient and robust queries. More ❯

Create sophisticated customer segmentation using behavioural, transactional, and demographic data Design and build predictive models to enhance personalized customer experiences across all channels Collaborate on design of test & learn methods to measure CRM initiatives' effectiveness Monitor and optimize model performance through continuous improvement cycles Technical Implementation Transform analytical solutions into production-ready code Implement models within our existing technology … recommendations to improve customer engagement metrics Skills ? Relevant experience in Customer Marketing Data Science including applied statistics and machine learning techniques (supervised and unsupervised learning, natural language processing, Bayesian statistics, time-series forecasting, collaborative filtering etc) ? Proficiency in Python with familiarity to ML libraries e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) ? Familiarity with cloud platforms (GCP More ❯

science libraries (NumPy, Pandas, Scikit-Learn) Experience with deep learning tools (e.g. PyTorch, TensorFlow, or similar) Strong mathematical/statistical background Familiarity with a wide range of data science methods (e.g. ML, NLP, Bayesian inference, APIs, SQL) Experience leading data science teams and managing project timelines Excellent communication and stakeholder engagement skills SC clearance and British More ❯

maintenance. The ideal candidate will be a good communicator and have a growth mindset with an enthusiasm for scientific discovery and strong interest in applying a combination of AI methods and mathematical models toward understanding mechanistic aspects of cellular processes with all experimental labs at Altos Labs. The ideal candidate is particularly interested in multi-scale (systems) biochemistry and … loop between experimental and theoretical work, at multiple scales, from molecules to cells, tissues and even whole organisms. Working at the interface between mathematical and computational models, and AI methods, with the aim of establishing design principles of rejuvenated cells. Collaborating with both experimental and computational scientists across Altos. Influence best practices in areas such as Bayesian … Biology, Computational Biology, Computer Science, or closely related field with strong emphasis in biological modeling. Relevant industry and/or academic experience. Expertise and a track record of using methods from artificial intelligence for biological design. Record of applications of dynamical systems to problems of synthetic biology. Record of applications of data driven modeling methods and AI to More ❯