1 to 25 of 33 Bayesian Methods Jobs in London

equivalent experience with experience in research Experience in client delivery with direct client contact Proven experience applying machine learning techniques to solve business problems Proven experience in translating technical methods to non-technical stakeholders Strong programming experience in python (R, Python, C++ optional) and the relevant analytics libraries (e.g., pandas, numpy, matplotlib, scikit-learn, statsmodels, pymc, pytorch/tf …/keras, langchain) Experience with version control (GitHub) ML experience with causality, Bayesian statistics & optimization, survival analysis, design of experiments, longitudinal analysis, surrogate models, transformers, Knowledge Graphs, Agents, Graph NNs, Deep Learning, computer vision Ability to write production code and object-oriented programming More ❯

with a minimum of 5 years in a leadership role, managing teams in dynamic and collaborative environments. Technical Skills: Proven expertise in clustering, predictive modelling, reinforcement learning, and Bayesian statistics. Experience in reinforcement learning and Agentic systems would be ideal Experience in ML Ops and deploying machine learning models at scale. Proficiency in Python or R, and More ❯

libraries of predictive features and probabilistic representations for diverse ML tasks. Build and optimize tools for scalable probabilistic inference under memory, latency, and compute constraints. Apply and innovate on methods like Bayesian neural networks , variational autoencoders , diffusion models , and Gaussian processes for modern AI use cases. Collaborate closely with product, engineering, and business teams to build … trends in generative modeling, causality, uncertainty quantification, and responsible AI. Requirements/Qualifications: Strong experience in Bayesian inference and probabilistic modeling : PGMs, HMMs, GPs, MCMC, variational methods, EM algorithms, etc. Proficiency in Python (must) and familiarity with PyMC, NumPyro, TensorFlow Probability , or similar probabilistic programming tools. Hands-on experience with classical ML and modern techniques, including … deep learning , transformers , diffusion models , and ensemble methods . Solid understanding of feature engineering, dimensionality reduction, model construction, validation, and calibration. Experience with uncertainty quantification and performance estimation (e.g., cross-validation, bootstrapping, Bayesian credible intervals). Familiarity with database and data processing tools (e.g., SQL, MongoDB, Spark, Pandas). Ability to translate ambiguous business problems into More ❯

outputs based on specific modelling, marketing, and business knowledge. Collaborate with subject matter experts and stakeholders to clarify business questions and enhance feature selection. Apply probability, frequentist vs Bayesian statistics, and statistical concepts like regression, ANOVA, AB testing. Select appropriate measurement techniques/designs to answer business questions and explain trade-offs. Define and build data models More ❯

stakeholders and translating research into practical solutions for predictive analytics. Experience in solution design, architecting and outlining data analytics pipelines and flows. Advanced Mathematics skills including experience with Bayesian statistics, linear algebra and MVT calculus, advanced data modelling and algorithm design experience. Design and deployment experience using Tensor Flow, Spark ML, CNTK, Torch or Caffe. The perks More ❯

Expertise in using data visualization tools such as Google Looker and working with Google's BigQuery. Experience in design and evaluation of A/B tests. Familiarity with Bayesian statistics, especially for hypothesis testing. Extra points for good knowledge of Bayesian programming (PyMC3, etc). Experience with application of optimization theory or reinforcement learning 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 overall data lifecycle of enterprise data from data acquisition or … biostatistics, or other quantitative field (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 (e.g., lasso, elastic net More ❯

analytics or media environments. 💡 Bonus Points For: Experience working with large datasets or cloud platforms (e.g. GCP/Big Query). Exposure to advanced analytics approaches such as Bayesian or non-regression models. Familiarity with marketing measurement tools or platforms (e.g. Robyn, Meridian). Data viz skills in Power BI, Tableau, or Data Studio. A background in More ❯

analytics or media environments. 💡 Bonus Points For: Experience working with large datasets or cloud platforms (e.g. GCP/Big Query). Exposure to advanced analytics approaches such as Bayesian or non-regression models. Familiarity with marketing measurement tools or platforms (e.g. Robyn, Meridian). Data viz skills in Power BI, Tableau, or Data Studio. A background in More ❯

mathematical competence and proficiency in statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning, model cross validation, Bayesian inference, time-series analysis, simple NLP, effective SQL database querying, or using/writing simple APIs for models. We regard the ability to develop new algorithms when an More ❯

and computational chemistry for drug discovery. The successful candidate will demonstrate strong abilities in cheminformatics and/or bioinformatics, including knowledge of established techniques and cutting-edge machine learning methods for modeling molecular properties and interactions with complex systems. They will also have experience with scientific programming and data science. These skills will be leveraged within a seasoned, agile … experience including hands-on experience with informatics, machine learning, and computational chemistry applied to drug discovery in the private sector, like biotech or pharma Experience with cheminformatics and bioinformatics methods (e.g., similarity/substructure searching, reaction-based enumeration, sequence alignment, etc.) Experience with molecular property prediction and multi-objective optimization using machine learning and/or deep learning methods … high-performance computing (GPU) environments for corporate R&D, innovation labs, or academic research An interest in solving scientific problems in chemistry and biology via computational and data-driven methods A drive to cooperate with colleagues to identify problems and communicate technical solutions in an accessible manner Hands-on mentality & comfortable with getting deep into the technical weeds of 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 ❯

managers and various business stakeholders to ensure top-quality outcomes to meet internal objectives. Investigate and adopt innovative concepts that offer tangible benefits. Employ techniques like Deep Learning, Bayesian Modelling, Large Language Models, Product embedding, Recommendation Systems, and Computer Vision. To be successful in the role, you'll need: 5+ years of hands-on data science experience. More ❯

mathematical competence and proficiency in statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning, model cross validation, Bayesian inference, time-series analysis, simple NLP, effective SQL database querying, or using/writing simple APIs for models. We regard the ability to develop new algorithms when an More ❯

mathematical competence and proficiency in statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning, model cross validation, Bayesian inference, time-series analysis, simple NLP, effective SQL database querying, or using/writing simple APIs for models. We regard the ability to develop new algorithms when an More ❯

mathematical competence and proficiency in statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning, model cross validation, Bayesian inference, time-series analysis, simple NLP, effective SQL database querying, or using/writing simple APIs for models. We regard the ability to develop new algorithms when an More ❯

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/… Architecture or related disciplines Strong 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, JAX or similar frameworks Strong More ❯

technical audiences. A self-starter who thrives in a fast-paced R&D environment. Experience in catastrophe modelling, especially around exposure and vulnerability. Applied statistical skills such as Bayesian statistics, uncertainty quantification, or Extreme Value Theory. Knowledge of machine learning applications in climate risk. Familiarity with cloud platforms (e.g., AWS, Google Cloud). If this role looks More ❯

technical audiences. A self-starter who thrives in a fast-paced R&D environment. Experience in catastrophe modelling, especially around exposure and vulnerability. Applied statistical skills such as Bayesian statistics, uncertainty quantification, or Extreme Value Theory. Knowledge of machine learning applications in climate risk. Familiarity with cloud platforms (e.g., AWS, Google Cloud). If this role looks More ❯

doing causal inference/segmentation Ability to explain ideas and present results to non-technical audiences Strong stakeholder management skills Experience with marketing mix models and/or Bayesian time series would be a big plus Strong theoretical understanding and experience with key classification and regression models is a plus Please note this role is offered on … doing causal inference/segmentation Ability to explain ideas and present results to non-technical audiences Strong stakeholder management skills Experience with marketing mix models and/or Bayesian time series would be a big plus Strong theoretical understanding and experience with key classification and regression models is a plus Please note this role is offered on More ❯

Science, Engineering, Statistics, Economics, Mathematics, 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 programs and culture within an More ❯

existing results in the broad field of machine learning. You will be apart of a highly unique research group across Natural Language Processing, Large Learning Models, deep learning, Bayesian & Signal Optimization with the exciting transition to the world of financial predication through the prism of machine learning. The most exceptional team members combine strong technical skills and More ❯

existing results in the broad field of machine learning. You will be apart of a highly unique research group across Natural Language Processing, Large Learning Models, deep learning, Bayesian & Signal Optimization with the exciting transition to the world of financial predication through the prism of machine learning. The most exceptional team members combine strong technical skills and More ❯

data structures using SQL or Python for data analysis. Statistical mindset with the ability to setup, run and analyse RCTs and split tests. Extra bonus for knowledge of Bayesian Statistics. Passionate about helping small businesses thrive-belief in our mission is important! The salary We expect to pay £55,000 - £70,000 for this role. However, we More ❯

data structures using SQL or Python for data analysis. Statistical mindset with the ability to setup, run and analyse RCTs and split tests. Extra bonus for knowledge of Bayesian Statistics. Experience optimising user journeys and driving customer engagement. Passionate about helping small businesses thrive-belief in our mission is important! The salary We expect to pay More ❯