18 of 18 Remote/Hybrid Bayesian Methods Jobs in London

business questions into analytical problems and build solutions using visualization techniques, dashboards, machine and deep learning, and statistical modelling. Search and develop new algorithms, methods, statistical models and business models, providing insight into structured and unstructured data. Apply R-based analytical and statistical techniques to solve business problemsApplying data … biostatistics, or other quantitative field (or equivalent).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 ...

business questions into analytical problems and build solutions using visualization techniques, dashboards, machine and deep learning, and statistical modelling. Search and develop new algorithms, methods, statistical models and business models, providing insight into structured and unstructured data. Apply R-based analytical and statistical techniques to solve business problems Applying … other quantitative field (or equivalent). 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 ...

Economics, Engineering etc.)Knowledge of advanced statistical concepts and techniques, e.g. skilled in linear algebra. Experience conducting hands-on analytics projects using advanced statistical methods such as generalized regression models, Bayesian methods, random forest, gradient boosting, neural networks, machine learning, clustering, or similar methodologies. Proficiency ...

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 ...

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 ...

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 ...

international clients, from data strategy and collection to modelling, insights, and activation.· Apply econometrics, regression, Bayesian statistics, and machine learning methods to analyse marketing and customer data· Collaborate closely with Managers to understand the project scope, structure, analytical approaches, manage operations and ensure timely delivery.· Build … similar tools.· Experience with Databricks, Azure, or other cloud/data platforms is a plus.· Solid understanding of econometrics, regression analysis, Bayesian statistics, or other advanced modelling techniques.· Experience translating data and model outputs into actionable business recommendations.· Comfortable handling large datasets, optimizing data pipelines, and delivering ...

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 ...

similar. ● PhD in Statistics or another area involving lots of work with messy data (e.g., Physics, Computational Biology). ● Extensive background in Bayesian inference, state-space models, filtering algorithms, etc. ● Excellent programming skills and experience implementing efficient algorithms for high-dimensional problems. Any prior exposure to sports ...

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 ...

supervised ML 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 ...

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 ...

work with product and engineering priorities. Nice to have Experience quantifying uncertainty in large models — calibration and confidence estimation via Bayesian methods, ensembling, steering, or prompt-based approaches. Experience with machine translation, multilingual NLP, or document-/layout-aware modelling experience. Familiarity with MoE-specific training ...

continuously learn and improve Desirable: Study design and sample size experience Regulatory experience A basic understanding or willingness to learn Bayesian methods and causal inference modelling Experience within Oncology and/or CNS therapeutic areas Opportunities at Veramed Presenting at conferences and authoring publications Developing and ...

continuously learn and improve Desirable: Study design and sample size experience Regulatory experience A basic understanding or willingness to learn Bayesian methods and causal inference modelling Experience within Oncology and/or CNS therapeutic areas Opportunities at Veramed Presenting at conferences and authoring publications Developing and ...

uncertainty quantification, experimental design, causal inference and Bayesian modeling. Just as essential is commercial instinct: the judgment to design rigorous, workable methods within real product goals and constraints, built through meaningful, hands-on experience shipping at startup speed. You will report to the CTO and work … experience. Strong statistical and probabilistic reasoning , with hands-on fluency in confidence intervals and hypothesis testing, power analysis, experimental design, causal inference, and Bayesian modeling. Experience designing evaluation methodologies - including black-box and counterfactual approaches - and the test datasets behind them, with good judgment on realism and ...

data transformations using distributed computing (e.g., Spark, Dask) and ensure robust test coverage for production systems. Mentor junior team members and utilise model explainability methods to drive feature performance improvements. About You 5+ years of experience in designing, building and maintaining machine learning models to solve complex, large-scale … business problems. Deep understanding of frequentist and Bayesian statistics, plus supervised and unsupervised modelling techniques. Proven experience modelling complex interactions (e.g., cluster or network effects). Expertise in model explainability to tune feature engineering and understand feature interactions. Ability to write high-quality, production-grade Python code ...

seeking a Robotics Software Engineer for our Online Calibration team to support scaling across our autonomous vehicle fleet. You will turn calibration and estimation methods into reliable, observable and resource-efficient components of the on-vehicle runtime, operating continuously on constrained, hardware-accelerated platforms. Working across calibration, state estimation … closely related robotics domain, including calibration of cameras, LiDAR, IMUs, vehicle parameters or multi-sensor systems. Strong understanding of filtering and estimation methods, such as Kalman-family filters, Bayesian estimation, smoothing, robust estimation or factor-graph optimisation. Strong modern C++ software-engineering skills and proficiency ...