1 to 25 of 34 Bayesian Methods Jobs

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 ❯

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 ❯

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 ❯

recommendations from appropriate data sources Experience with sensor data such as Radar and various infrared sensors Experience developing and implementing probabilistic models to combine sensor data such as Bayesian reasoning, Kalman filtering and evidence theory Experience with advanced probability statistics and applying in real world scenarios Travel: 10% Drug Free Workplace: Boeing is a Drug Free Workplace More ❯

recommendations from appropriate data sources Experience with sensor data such as Radar and various infrared sensors Experience developing and implementing probabilistic models to combine sensor data such as Bayesian reasoning, Kalman filtering and evidence theory Experience with advanced probability statistics and applying in real world scenarios Travel: 10% Drug Free Workplace: Boeing is a Drug Free Workplace More ❯

recommendations from appropriate data sources Experience with sensor data such as Radar and various infrared sensors Experience developing and implementing probabilistic models to combine sensor data such as Bayesian reasoning, Kalman filtering and evidence theory Experience with advanced probability statistics and applying in real world scenarios Travel: 10% Drug Free Workplace: Boeing is a Drug Free Workplace 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 ❯

attributes 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 Visualization, data skills, SQL, matplotlib, etc. Benefits We are a well treated bunch, with awesome benefits! If there's something important More ❯

Several years of data science expertise, ideally within the supply chain domain A background in consulting and/or working within the cpg/retail industries Knowledge of Bayesian statistics, but broader ML experience, e.g. time series modeling and forecasting will be considered Strong communication skills, with the ability to distil and translate complex concepts into simple 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 ❯

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 ❯