76 to 100 of 117 Bayesian Methods Jobs

Join us as a Data Scientist, PBWMIn this role, you’ll drive and embed the design and implementation of data science tools and methods, which harness our data to drive market-leading purpose customer solutionsDay-to-day, you’ll act as a subject matter expert and articulate advanced data … statistics are also requiredExpertise in developing Gen AI, Agentic AI tools for chat bots in addition to understanding workflows and evaluation methods to measure and monitor performance at scaleManaging ML, AI models in production with experience of CI/CD, DevOps, and model monitoring with strong ...

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 ...

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 ...

Master's) in Statistics, Mathematics, Physics, or similar. You reason about uncertainty and calibration as first-order concerns.Research mindset. You're curious about new methods and actively look for better ways to do things. You try them out, not just read about them.Judgement. You critically evaluate model output – yours … modelling. You have used or contributed to models that quantify channel impact or optimise spend.Bayesian methods. You have used hierarchical models, MCMC, or Bayesian updating in real work.Production ML. You have built and shipped supervised ML models end to end – exploration, training, deployment, monitoring.Python. The stack ...

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 ...

oversee a small team of quantitative experts delivering advanced trial design, modelling, and predictive analytics. The role drives digital twins, external controls, and Bayesian borrowing, while ensuring regulatory alignment and high-impact use cases. Collaboration with Biometry Leadership is essential. #J-18808-Ljbffr ...

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 ...

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 ...

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 ...

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 ...

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 ...

Role We are looking for a biostatistician with expertise in Bayesian modelling to work on probabilistic modelling of physiological data and medical risk. The role focuses on developing statistical frameworks that combine multiple data sources to estimate the probability of life-threatening deterioration and support triage prioritisation. … engineers, medical advisors, and the product team to design models that operate in real-world environments with noisy and incomplete data. Responsibilities Develop Bayesian statistical models to estimate physiological deterioration risk from multi-sensor data Build probabilistic frameworks integrating vital signs, haemodynamic indicators, motion data, and environmental ...

Build production-grade Python packages Follow and build project scaffolds (PowerShell), follow coding standards, and reproducible workflows Transform analyses into standardized currently best-known methods (cBKM) Process data and automate data flows; manage and optimize data structure & data bases for big data sets Interact with product and service teams … Bachelor or Master Degree: Statistics/Computer Science/Data Science//Industrial Engineering Expert knowledge of Python, statistical modelling (frequentist and Bayesian) and data visualization Intermediate knowledge of PowerShell, R and SQL AI expert knowledge Project management Comfortable with non-recurrent, high-complexity engineering analyses ...

and strategic level. Experience delivering marketing analytics e.g., Marketing Mix Modeling , geo experiments, meta-analysis, audience segmentation and propensity modeling . Understanding of Bayesian approaches and modeling frameworks. Proven ability to generate practical solutions for marketing analytics problems and use results to drive business change in partnership … talent. Responsibilities Provide support in media strategy, measurement and optimization that require expertise in advanced analytics work, with special focus on marketing analytics methods and data science. Partner with internal teams in advanced analytics work including experimentation, measurement and modeling. Identify patterns and behaviors that are effective predictors ...

tools and datasets to characterise small-molecule interactions with intrinsically disordered proteins. Based just a short walk from Kings Cross station, Bind leverages interdisciplinary methods that span experimental biophysics – with a strong focus on nuclear magnetic resonance (NMR) spectroscopy – as well as computational approaches and cellular studies. You will … built models by implementing automated, streamlined, and efficient software solutions in line with best practices Utilise active learning, Bayesian, and bootstrapping methods to achieve robust performance in low-data regimes, and make use of distributed training methodologies for large models Build model-deployment and job-launching ...

data and audiences we build for. Comfortable with sport-driven modelling decisions. Machine Learning & Statistics: Solid grounding in machine learning, supervised and unsupervised methods, and classical statistical techniques. Comfortable working with probabilistic models, uncertainty estimation and Bayesian inference. Model Development: Understanding of the full model training … Engineering: Experience building and maintaining production data pipelines, or working with AWS services such as Lambda, EventBridge and DynamoDB. Simulation: Experience with Monte Carlo methods or probabilistic simulation. AI Integration: Comfortable using AI-assisted coding tools such as Claude Code or Cursor as part of your everyday workflow, 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 … product, and customer context. You need complete information before acting, rather than exercising judgment under uncertainty and evolving guidance. You'd rather follow established methods than create structure from ambiguity; defining and refining our evaluation process is core to the job. You're uncomfortable owning the quality bar and ...

mission. You’ll excel if You have experience in any of: control systems, time-series modelling, reinforcement learning, system identification, optimisation or bayesian statistics. You are used to working in a fast-paced startup environment with an agile … process. You have a degree in machine learning, physics, chemistry, engineering, mathematics or computer science. You have a track record of developing novel ML methods in relevant fields. You are hungry for responsibility, enthusiastic about taking on the design and development of solutions to difficult problems, and eager ...

automation. The 45% is the distinguishing feature: a candidate who can run the programme but cannot challenge a metric definition or assess a Bayesian approach will deliver roughly half of what the role requires. You'll collaborate with Product Analytics, Analytics Engineering, Engineering leaders and Trade partners … Product, Engineering, Analytics, Trade and Data to keep experimentation aligned to company goals, communicating vision and outcomes clearly across the business. Champion quasi-experimental methods (e.g. diff-in-diff, synthetic control, matched market tests) for scenarios where randomised testing isn't feasible, and build a playbook to guide their ...

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 ...

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 ...

algorithms that power Biostream’s physiological monitoring and alert systems. You will work closely with the biostatistics team to translate probabilistic and Bayesian models into deployable algorithms that operate on real-world sensor data. The role focuses on building robust machine learning systems capable of detecting physiological … constrained connectivity. Responsibilities Design and develop machine learning algorithms for physiological signal analysis and risk detection Work closely with the biostatistician to implement Bayesian models and probabilistic frameworks Develop models combining vital signs, motion data, and contextual signals Build pipelines for sensor data processing, feature extraction, and ...

website). Key responsibilities Develop your own research program, with a view to developing new tools and techniques for probabilistic models, Bayesian optimization and related fields. Engage in team collaborations to meet research goals and report your research findings both internally and externally. Contribute to internal product … advantage: A background in decision making (Bayesian optimization, bandits, reinforcement learning, active learning) and/or probabilistic modelling and methods (Gaussian processes, Bayesian neural networks, Variational inference, etc). Experience in numerical programming (Python/NumPy/Tensorflow/PyTorch). Experience ...

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 ...