1 to 25 of 74 Permanent Bayesian Methods Jobs in London

through clear documentation and transparent methodology. Ensure models meet high standards for both statistical correctness and practical applicability. Implement calibration techniques and uncertainty estimation methods where appropriate. Evaluate model behaviour under different market environments and changing economic conditions. LLM and Document Intelligence Solutions Build AI systems that extract structured … techniques. Uncertainty quantification. Model explainability methods. Handling imbalanced datasets. Missing data treatment and imputation. Distribution shift analysis. Time‐series modelling. Forecasting methodologies. Advanced Quantitative Methods Bayesian approaches. State‐space models. Regime‐switching frameworks. Hierarchical modelling. Multi‐task learning. Survival analysis. Hazard models. Event‐driven modelling. Rare ...

related to consumer behaviour, market trends, and campaign performance. Build, maintain, and apply statistical and predictive models, including regression, econometrics, Bayesian methods, and Marketing Mix Modelling (MMM). Develop dashboards, charts, and visualizations to communicate insights effectively to clients and internal teams. Assist in developing marketing ...

least 3 data or ML products from problem definition to production deployment and monitoring. Demonstrated proficiency in clustering, predictive modeling, reinforcement learning, and Bayesian statistics. Hands-on experience in software engineering, MLOps, and deploying machine learning models at scale. Proficiency in SQL, Python, and familiarity with ...

least 3 data or ML products from problem definition to production deployment and monitoring.* Demonstrated proficiency in clustering, predictive modeling, reinforcement learning, and Bayesian statistics.* Hands-on experience in software engineering, MLOps, and deploying machine learning models at scale.* Proficiency in SQL, Python, and familiarity with ...

deep learning, neural networks, reinforcement learning, and transfer learning.Familiarity with traditional machine learning algorithms such as random forests, SVM, logistic regression, and Bayesian modelling for model building, validation, and testing.Familiarity with cloud platforms (e.g., Bedrock, AWS, Azure) for model deployment and the creation of production-ready pipelines.Proficiency ...

deep learning, neural networks, reinforcement learning, and transfer learning. Familiarity with traditional machine learning algorithms such as random forests, SVM, logistic regression, and Bayesian modelling for model building, validation, and testing. Familiarity with cloud platforms (e.g., Bedrock, AWS, Azure) for model deployment and the creation of production ...

fundamentals Advanced data skills: statistical modelling, machine learning, time‐series forecasting, demand prediction, optimisation, inventory management Experience with dynamic pricing, stochastic processes, or Bayesian inference is a plus Preferred Experience Background in pricing, forecasting or optimisation roles within airlines, hospitality, car rentals, e‐commerce, or similar dynamic ...

Excel, and visualisation tools; experience with Databricks, Azure, or other cloud/data platforms is a plus. Strong understanding of econometrics, regression, Bayesian statistics, and advanced modelling techniques. Skilled in handling large datasets, optimising pipelines, and delivering high-quality outputs Track record of translating complex model results ...

/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 ...

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 ...

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 ...

experimental data, and in your penultimate year of study. Experience working with datasets using Python, R, SAS, MATLAB, or other statistical software. Experience with methods of statistical data analysis (e.g., linear models, hypothesis testing, multi-level models, Bayesian methods, etc.). Preferred qualifications: Experience with … search techniques. Produce quantitative and qualitative modeling of business dynamics, user behavior, and more. Identify areas for further investigation as well as creating innovative methods of analysis. Please complete your application before 23rd October 2026. We encourage you to apply as early as possible as we review applications ...

and non-technical stakeholders. Collaborative mindset and ability to work across data science, engineering, and business teams. Desirable Ability to build and evaluate Bayesian models using PyMC or Stan, with a focus on real-world forecasting applications. Formal work experience not required. Experience in pricing analytics, demand ...

view on where it helps and where it doesn't.The requirementsEssential:Statistical foundations. Strong grounding in probability and statistics, with an understanding of Bayesian reasoning and experience applying hierarchical modelling or decision-making under uncertainty.Causal inference. Experience separating signal from noise in observational and experimental data.Business translation. ...

experience in managing installable python packages is very valuable. Proficient SQL and database knowledge and experience. Advanced knowledge of time-series modelling or Bayesian statistics. Experience working with AWS. Experience creating visualisations with Power BI. Proficiency with Git. Experience/proficiency with Docker. Good experience/knowledge ...

think in terms of the cost function and inductive biases of your modelsBayesian methods. You have used hierarchical models, MCMC, or Bayesian updating in real work.Time series modelling. You have modelled temporal data where autocorrelation, drift, or seasonality mattered.Python. The stack the team uses.The salaryWe expect ...

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 ...

Track record of promoting statistical rigour and experimental best practices in your prior roles. Prior experience using causal inference/econometric techniques and bayesian methodologies for hypothesis testing. Prior experience using large datasets with distributed computing (e.g. spark, hadoop or other map-reduce tech) Experience working ...

production.Track record of promoting statistical rigour and experimental best practices in your prior roles.Prior experience using causal inference/econometric techniques and bayesian methodologies for hypothesis testing.Prior experience using large datasets with distributed computing (e.g. spark, hadoop or other map-reduce tech)Experience working in a fast ...

Stats Background • Outstanding background in probability and statistics • Experience and interest in modern machine learning techniques, for example, deep learning, Bayesian methods and graphical models • Programming experience with python, julia or R You will be joining a progressive and exciting company committed to excellence. They offer ...

Tools: C++, Python, Git, Docker, CI/CD, Linux, Docker/Podman ML specific skills Stochastic processes: behavior trees, FSMs, MDPs Learning: RL, Bayesian networks (and BBP) Scripting languages: PDDL, RDDL Nice to Haves Experience with SLAM, computer vision applied to UAVs Knowledge of AI safety principles ...

data pipelines using dbt, Airflow, and Snowflake. Beyond querying, you use Python for advanced automation and forecasting, and you apply statistical rigor (e.g., Bayesian statistics, causal inference) to solve complex product problems. Commercial & Risk Mindset: You possess deep commercial fluency, understanding the unit economics of a fintech ...

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 ...