51 to 62 of 62 Bayesian Methods Jobs in England

cloud-native stack (Snowflake). As an experienced technical expert and researcher, you'll lead on individual projects that apply advanced predictive and prescriptive methods (such as causal inference, stochastic simulations, Bayesian hierarchical modelling, machine learning and more) to real-world operational and strategic questions such … tools, considering user needs, data quality and management, user experience and engagement and making best use of statistical modelling and/or machine learning methods and working with other team members to deployment and maintain these Use open-source analytical tools (including R or Python) in an open and ...

research‐heavy MSc in Physics, Computer Science, Applied Mathematics or a related field. Familiarity with probabilistic algorithms, ideally also having implemented MCMC methods, Gibbs sampling, and energy‐based models. Familiarity with Bayesian inference, Causal AI, and the mathematical foundations of Boltzmann machines. Ability to review Python ...

quality, customer effort, process adherence, consistency, fairness, durability of resolution and cost effectiveness. Identify the root causes of issues by combining machine learning, statistical methods and domain expertise from colleagues to understand what genuinely drives customer outcomes. Identify and test solutions Turn analytical insight into concrete proposals to improve … Python skills, with experience working with large, complex and imperfect datasets. Strong grounding in statistical analysis (e.g., classical ANOVA, non-parametric uncertainty quantification, Bayesian estimation) and causal inference, with experience designing both controlled experiments and observational studies. Experience applying machine learning, statistical modelling, NLP and LLM-based ...

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 ...

risk, lending, or customer lifetime value modelling. Non-linear methods. You have shipped gradient boosting or neural networks on tabular data in production. Bayesian methods. You have used hierarchical models, MCMC, or Bayesian updating in real work. Time series modelling. You have modelled temporal ...

harness massive compute power and to use state-of-the-art ML techniques – published in recent conferences or developed entirely in-house – as textbook methods won't beat the competition. ML is integral to develop successful investment management strategies; it is one of the core drivers of our overall … learning, non-convex optimisation, Bayesian non-parametrics, NLP or approximate inferenceExcellent reasoning skills and mathematical ability are crucial: off-the-shelf methods don't always work on our data so you will need to understand how to develop your own modelsStrong programming skills and experience working ...

multi-modal data ingestion and distribution modelling to production-grade visualisation dashboards. Must-have experience: Probabilistic Time Series Forecasting: Experience using probabilistic time series methods (eg, Bayesian, PyMC, Amazon DeepAR) on small or scarce datasets for distribution prediction. Data Lake & DBaaS Integration: Proven ability to build ...

Customer Lifetime Value modelling projects end to end Identify where the modelling stack is holding the business back Frame modelling opportunities and select appropriate methods Keep production models healthy through monitoring and maintenance Deliver incremental model development and research that reshapes model approaches Set technical direction on multi-quarter … bonus Experience shipping gradient boosting or neural networks on tabular data in production is a bonus Experience with hierarchical models, MCMC, or Bayesian updating is a bonus Experience modelling temporal data involving autocorrelation, drift, or seasonality is a bonus Python experience is a bonus Core Competencies Demonstrates ...

bonus Understanding of model cost functions and inductive biases is a bonus Experience with hierarchical models, MCMC, or Bayesian updating is a bonus Experience modelling temporal data involving autocorrelation, drift, or seasonality is a bonus Python experience is a bonus Core Competencies Demonstrates expertise in credit risk … Risk Modelling Customer Lifetime Value Modelling Machine Learning Model Development Causal Estimation Python Programming Hard Skills Probability Statistics Model Evaluation Hierarchical Models MCMC Bayesian Updating Temporal Data Modelling Inductive Biases Automation Research Methodology Soft Skills Critical Thinking Communication Problem Solving Analytical Mindset Collaboration Industry Keywords Lending Credit ...

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 ...

each stage of the consumer's lifecycle. Experience overseeing experimentation programmes and practices that inform product or business decisions. A good understanding of methods for statistical inference (e.g. Bayesian, Frequentist) and familiarity with predictive analytics techniques is highly beneficial. Working knowledge of common analytics tools used ...

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 ...