26 to 45 of 45 Bayesian Methods Jobs in London

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 ...

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 ...

shaping a modern, cloud-native stack from an early stage. As an experienced technical leader and researcher, you'll use advanced predictive and prescriptive methods (causal inference, stochastic simulation, Bayesian hierarchical modelling, machine learning and more) to tackle real strategic and operational questions, e.g., forecasting …/equivalent experience in Data/analytics field Evidence of post qualifying and continuing professional development Demonstrable knowledge in relevant analytical fields, their associated methods, and their potential for application within healthcare, including advanced statistics, epidemiology, data science, econometrics, and operational research Expert knowledge of the role of analysis ...

duties and responsibilities will include, but won't be limited to building and improving sports prediction modelsTo research recent advances in Bayesian updating to better extract signals from noisy dataTo design and build your own models using your own ideasTo work closely with expert traders and developers ...

computer science, or equivalent experience with experience in research 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 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 vision Ability to write production code and object-oriented ...

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 ...

bookmaker) Experience with and/or knowledge of Bayesian models, state space models, filtering and smoothing, computational statistics and approximate inference methods Experience with and/or knowledge of machine and statistical learning, deep neural networks, feature engineering, reinforcement learning, dynamic optimisation and optimal control Experience ...

analyses, develop protocols, and provide senior statistical leadership across multiple engagements with clients. The role requires hands-on expertise in Bayesian methods, ITCs, survival analyses, and programming in R and BUGS/JAGS/Stan. Collaboration across geographies is essential, with mentoring duties and business development ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...