26 to 50 of 58 Bayesian Methods Jobs

that ships it. You'll work closely with Phil Cowans, our fractional CTO, who co-founded Songkick and Nested as CTO and studied Bayesian machine learning at Cambridge (the maths behind probabilistic risk pricing). The platform is in production on a Python backend and a React ...

fintech and Cleo's mission to make money easier and more human Nice to have: causal inference depth (CUPED, Bayesian methods, PSM, uplift modelling) and the judgement to know when it's worth the effort Nice to have: genuine enthusiasm for agentic AI as a force ...

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 Benefits Competitive salary and equity in a fast‐growing start‐up We serve lunch every weekday ...

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 ...

instinct to every sprint without being asked, and who are comfortable with problems that do not have a known answer. The work involves Bayesian statistical modeling, experiment design, and data problems with no off-the-shelf answer. Experience with agentic systems matters: understanding how model outputs feed … production handoff), working under the direction of the Tech Lead and in close partnership with the Full-stack Engineer Design, build, and validate Bayesian statistical models, time-series forecasts, and domain-specific analytical frameworks (marketing mix models, brand equity models, price elasticity and promotional lift models) that ...

propensity profiles.The work will focus on AI Evaluation and will be drawing from multiple disciplines, but primarily from artificial intelligence, technical AI safety, Bayesian statistics and causal modelling, psychology and AI governance.Appointment at the Research Associate level is dependent on the award of a PhD. This ...

sitting at the intersection of modern machine learning and quantum computing — with two complementary directions. ML for quantum — using neural networks and learning-based methods to accelerate quantum computation (e.g. circuit optimisation and compilation, error mitigation, calibration, variational-parameter optimisation, and surrogate modelling of quantum processes). …/with quantum — building and adapting classical AI methods, including neural networks and large language models, so they can benefit from quantum hardware and algorithms. You don't need to be a world expert in either quantum or LLMs — but you should be genuinely comfortable with neural networks and ...

initiative and are deeply accountable to your internal customers and engineering colleagues. Bonus: experience with implementing solutions for adaptive experimentation ML (contextual bandits, Bayesian optimization or RL-based allocation) Additional Information Bring all of you to work We create the conditions for high performers to thrive, through ...

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 ...

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 ...

Fundamentals, Manchester, UK. These lectureships form part of a strategic investment in fundamental AI research; candidates should have strong backgrounds in Bayesian inference, automatic experimental design, human-in-the-loop learning, machine teaching, privacy-preserving learning, reinforcement learning, and related areas. #J-18808-Ljbffr ...

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 ...

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 ...

Overview In this role, you will drive data-driven marketing insights by building and operationalizing advanced models, notably Bayesian Media Mix Models (MMM) and ROI frameworks. You'll be the technical engine behind measuring marketing impact, guiding multi-channel growth, and ensuring model reliability at scale. Collaborating … options programme 25 days of paid annual leave Newest MacBook models Confidential Employee Assistance Program (EAP) Annual learning budget Hybrid Work Verantwoordelijkheden Evolve Bayesian MMMs and ROI frameworks to deliver precise business insights Build and maintain econometric and statistical models (MMM, GMV, engagement) to quantify marketing impact ...

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 ...

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 ...

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 ...

order to improve Eligible to work in London. And any of these would be great, but aren’t essential: Experience in applying machine learning methods to real-world problems Familiarity with supervised ML Familiarity with Time Series Forecasting Familiarity with supervised LLM chat and workflow automation Familiarity with Computer … Vision Familiarity with Bayesian Learning Familiarity with Causal Inference Familiarity with Recommender Systems Familiarity with Graphs and GNNs Familiarity with Reinforcement Learning Familiarity with Apache Spark or other distributed processing frameworks A paid 6-week sabbatical leave after four years For everyone, everywhere. We’re people building ...

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 ...

and effective mental health/wellbeing support for those most in need. You will contribute to research to test and improve how automated evaluation methods (e.g., patient simulation, LLM-judging) reflect people and languages in different settings, and to make effective use of local clinicians and lived experience input. … settings, how much different experts agree/disagree, and how plurality of opinion can be represented in our evaluations. Developing and adapting automated evaluation methods to reflect how people in each setting talk about their mental health and seek support from AI, including in local languages and where people ...

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 ...

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 ...

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 ...