1 to 25 of 32 Bayesian Methods Jobs in England

both technical and non-technical audiences. A proven track record of turning business problems into measurable outcomes. Nice to Have Experience with Bayesian modelling, optimisation algorithms or multi-armed bandits. Exposure to Generative AI, LLMs or agentic AI systems. MLOps experience, including CI/CD, model monitoring ...

technical standard for experimentation across Product Data Science, from conventional A/B testing to quasi-experimental and Bayesian methods, and make it practical through protocols, frameworks and tooling. Partner with Product, Engineering and Data Platform teams to improve experimentation velocity without sacrificing rigour. Critically assess … organisation. Statistical & Experimentation Expertise: Authoritative command of experimentation in all its forms, from experimental design and variance reduction to causal inference, bandits and Bayesian methods. You should be able to develop and validate methodology, not only apply it. Technical & Modelling Expertise: Expert level proficiency in Python and ...

and deploy production‐grade ML systems with scalability and robustness in mind. Translate state‐of‐the‐art research (probabilistic modelling, Bayesian methods, optimisation) into customer‐ready solutions. Work collaboratively within a cross‐functional team to deliver measurable impact to global shipping customers. Communicate complex ideas clearly … What We’re Looking For Familiarity and some experience with machine learning research or applied ML, ideally with expertise in probabilistic models and Bayesian statistics. Experience bringing ML models from prototype to production with supervision. Strong analytical and problem‐solving abilities, and confidence working across disciplines following ...

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 ...

pricing models. Turning pricing opportunities into measurable hypotheses, experiments and production changes. Evaluating pricing performance through backtesting, online testing and commercial metrics. Developing Bayesian, multi-armed bandit and other optimisation approaches for dynamic pricing. Working with commercial, finance and product teams to understand pricing performance and recommend … code, testing and code review, alongside strong SQL. A solid grounding in machine learning techniques such as gradient boosting, GLMs, Bayesian methods and multi-armed bandits, or comparable experimentation and optimisation methods. Experience designing, running and evaluating pricing experiments using both commercial and statistical measures. ...

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 At Fin, we want to give people what they need to do the best work ...

that ships it. You'll work closely with Phil Cowans, our fractional CTO, who co-founded Songkick and Nested as CTO and studied Bayesian machine learning at Cambridge (the maths behind probabilistic risk pricing). The platform is in production on a Python backend and a React ...

shipping ML productsPhD or other experience in a research environmentDeep experience in an applicable ML area - E.g. NLP, Deep learning, Bayesian methods, Reinforcement learning, clusteringStrong stats or math backgroundVisualization, data skills, SQL, matplotlib, etc. Unleash Your PotentialWhen you join Salesforce, you'll be limitless ...

and program-level ROI storytelling. Statistical Rigor & Methodology Oversight Act as a credible thought partner to your statisticians: understand the tradeoffs across classical, Bayesian, and causal-inference approaches well enough to review their work, defend it to customers, and know when to push back. Set and enforce … Comfortable partnering with a statistics team - you don't need to derive the models yourself, but you understand classical inference, Bayesian methods, or causal inference well enough to be a credible reviewer and thought partner. Hands-on experience building AI agents or agentic workflows, not just ...

algorithms, tuning, and performance evaluation. Experience with a range of feature engineering techniques (e.g., target encoding). Solid grasp of frequentist and Bayesian statistics for parameter estimation and experimentation. Experience in writing clean, production‐grade Python code for both model training and inference. Excited to leverage LLMs ...

with a proven record of success in online data science competitions, such as Kaggle. Experience in deep learning, reinforcement learning, non‐convex optimisation, Bayesian non‐parametrics, NLP or approximate inference. Excellent reasoning skills and mathematical ability. Strong programming skills and experience working with Python, Scikit‐Learn, SciPy ...

and a training loop, and who has built systems from scratch because they wanted to understand how they work. Key Responsibilities: Build RL, Bayesian and LLM-agent-driven design-space exploration over gem5 and cycle-accurate models, using fast simulation for broad search and detailed models ...

initiative and are deeply accountable to your internal customers and engineering colleagues. Bonus: experience with implementing solutions for adaptive experimentation ML (contextual bandits, Bayesian optimization or RL-based allocation) Additional Information Bring all of you to work We create the conditions for high performers to thrive, through ...

teams to design and implement new statistical and machine learning features across Optimizely’s product suite. Conduct rigorous testing and validation of new statistical methods and ensure seamless integration into the platform. Analyze product performance data to optimize existing features and provide actionable recommendations for improvement. Leverage AI agents … deploy new statistical capabilities. Research and Innovation Partner with academic and industry researchers to explore cutting-edge statistical methods and machine learning innovations. Identify emerging trends in experimentation and statistics that could enhance Optimizely’s offerings. Propose and prototype advanced statistical techniques to solve challenging business problems faced ...

sitting at the intersection of modern machine learning and quantum computing — with two complementary directions. ML for quantum — using neural networks and learning-based methods to accelerate quantum computation (e.g. circuit optimisation and compilation, error mitigation, calibration, variational-parameter optimisation, and surrogate modelling of quantum processes). …/with quantum — building and adapting classical AI methods, including neural networks and large language models, so they can benefit from quantum hardware and algorithms. You don't need to be a world expert in either quantum or LLMs — but you should be genuinely comfortable with neural networks and ...

firms and telecoms; including Nissan, L'Oréal, Carrefour, WHSmith, Orange, Beiersdorf, BNP Paribas, and Samsung. Our success stems from combining advanced data technologies, agile methods for quick delivery, and dedicated teams of data scientists, data engineers, business consultants, and data analysts. Our 1,800 employees operate in 25 countries … client stakeholders. Advanced Modelling: Proven ability to implement a range of complex models such as time-series forecasting, gradient boosting, clustering, NLP, and Bayesian inference. ML-Ops & Orchestration: Strong experience with MLOps tools for orchestration, experiment tracking, hyper-parameter tuning, and deploying automated model retraining pipelines. Programming ...

Postdoc and Research Fellow positions in Machine Learning, Centre for AI Fundamentals, Manchester, UK Keywords include automatic experimental design, Bayesian inference, human-in-the-loop learning, machine teaching, privacy-preserving learning, reinforcement learning, inverse reinforcement learning, computational rationality and user modelling, and simulator-based inference. More background ...

engineers, and industry experts to deliver real customer value. The role blends research and production, requiring a strong ML foundation, Bayesian methods, and ability to translate research into practical solutions. Hybrid work in London offered. #J-18808-Ljbffr ...

International Society for Bayesian Analysis is seeking to fill research positions in machine learning at various levels at the University of Manchester. Candidates will engage in groundbreaking research funded by the Turing AI World-Leading Researcher Fellowship, focusing on probabilistic modelling in diverse areas including human ...

Fundamentals, Manchester, UK. These lectureships form part of a strategic investment in fundamental AI research; candidates should have strong backgrounds in Bayesian inference, automatic experimental design, human-in-the-loop learning, machine teaching, privacy-preserving learning, reinforcement learning, and related areas. #J-18808-Ljbffr ...

built that other people went on to use Broad training in empirical method, potentially from a field outside machine learning – experimental design, Bayesian inference, model selection, significance testing, uncertainty quantification Experience with automated grading and model-based judging, and a rigorous account of its failure modes and ...

organisation. Statistical & Experimentation Expertise: Authoritative command of experimentation in all its forms, from experimental design and variance reduction to causal inference, bandits and Bayesian methods. You should be able to develop and validate methodology, not only apply it. Technical & Modelling Expertise: Expert level proficiency in Python and … and growth over time, and to bring that perspective into how experiments are designed and interpreted. Standardising at Scale: Experience standardising processes, frameworks or methods across multiple teams, including the use of AI and automation to make good practice the default. Cross‐Functional Partnership: Proven ability to build strong ...

Engineers where forecasting intersects agentic workflow, including proactive variance alerting, hypothesis ranking and draft narrative generation, while retaining appropriate finance review and approval. Document methods, data, assumptions, model limitations and validation evidence, and measure whether the solution improves decision quality, planning efficiency or forecast performance in use. Essential experience … Depth in time series and forecasting methods, spanning classical and modern approaches, with the judgement to select appropriately, including seasonality, external regressors, rolling horizons and model trade-offs. Python and the associated analytical stack, with experience of production or near-production deployment, together with SQL, Git, testing and reproducible ...

statistics, mathematics, computer science, or equivalent experience with experience in researchProven experience applying machine learning techniques to solve business problemsProven experience in translating technical methods to non-technical stakeholdersStrong programming experience in Python and the relevant analytics libraries (e.g., pandas, numpy, matplotlib, scikit-learn, statsmodels, pymc, pytorch/…/keras, langchain). R and C++ optional. 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 visionAbility to write production code and object-oriented programming ...

velocity, and tackle complex scenarios where traditional A/B tests fall short. You will build robust metric systems, apply Bayesian methods and multi-arm bandits, and mentor junior data scientists while ensuring products scale safely across markets and time. #J-18808-Ljbffr ...