22 of 22 Remote Bayesian Methods Jobs

Understands complex and critical business problems from a variety of stakeholders and business functions, formulate integrated analytical approach to mine data sources, employ statistical methods and machine learning algorithms to contribute solving unmet medical needs, discover actionable insights and automate process for reducing effort and time for repeated use. … or equivalent). More than 6 years 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 More ❯

Python skills; familiarity with R for MMM. Expertise in regression modeling, statistical and ML techniques . Experience with probabilistic programming, Bayesian methods, and MCMC. Proficient in SQL and/or Spark for large-scale data mining. Solid understanding of statistical foundations and mathematical modelling. Familiarity with More ❯

Python skills; familiarity with R for MMM. Expertise in regression modeling, statistical and ML techniques . Experience with probabilistic programming, Bayesian methods, and MCMC. Proficient in SQL and/or Spark for large-scale data mining. Solid understanding of statistical foundations and mathematical modelling. Familiarity with More ❯

practical solutions for predictive analytics. Experience in solution design, architecting and outlining data analytics pipelines and flows. Advanced Mathematics skills including experience with Bayesian statistics, linear algebra and MVT calculus, advanced data modelling and algorithm design experience. Design and deployment experience using Tensor Flow, Spark ML, CNTK More ❯

reinforcement learning frameworks such as OpenAI Gym or Stable-Baselines3. Practical knowledge of optimization algorithms and probabilistic modeling techniques (e.g., Bayesian methods, Gaussian Belief Propagation). Experience integrating models into real-time decision-making systems or multi-agent RL environments (MARL). Exposure to spatiotemporal data More ❯

statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning, model cross validation, Bayesian inference, time-series analysis, simple NLP, effective SQL database querying, or using/writing simple APIs for models. We regard the ability to More ❯

Ability to explain ideas and present results to non-technical audiences. Strong stakeholder management skills. Experience with marketing mix models and/or Bayesian time series would be a big plus. Strong theoretical understanding and experience with key classification and regression models is a plus. By joining More ❯

environment constantly subject to innovation. How will you make an impact: Estimating uncertainty and error propagation in our models; Applying Bayesian methods for the calibration of complex process-based models; Designing sampling strategies for our data collection campaigns; Evaluating the quality and quantity of our data … in one of the relevant Science fields mentioned above; A baseline understanding of greenhouse gas accounting frameworks and methodologies; Strong theoretical background in Bayesian Statistics; Knowledge or experience working with crop, pedometrics, or process-based models; Experience coding in Python; Handling unstructured/imbalanced data. What's More ❯

probabilistic representations for diverse ML tasks. Build and optimize tools for scalable probabilistic inference under memory, latency, and compute constraints. Apply and innovate on methods like Bayesian neural networks , variational autoencoders , diffusion models , and Gaussian processes for modern AI use cases. Collaborate closely with product, engineering … uncertainty quantification, and responsible AI. Requirements/Qualifications: Strong experience in Bayesian inference and probabilistic modeling : PGMs, HMMs, GPs, MCMC, variational methods, EM algorithms, etc. Proficiency in Python (must) and familiarity with PyMC, NumPyro, TensorFlow Probability , or similar probabilistic programming tools. Hands-on experience with classical … ML and modern techniques, including deep learning , transformers , diffusion models , and ensemble methods . Solid understanding of feature engineering, dimensionality reduction, model construction, validation, and calibration. Experience with uncertainty quantification and performance estimation (e.g., cross-validation, bootstrapping, Bayesian credible intervals). Familiarity with database and data More ❯

projects, and guiding collaborative efforts is a plus. Knowledge of the following machine learning domains is a plus: Generative models leveraging diffusion or Bayesian Flow Networks. Large-scale distributed machine learning training. Knowledge, experience or interest in the following biological domains is a plus: Drug discovery and More ❯

CI/CD, GCP. Understanding algorithms & data structures; software & system design principles. Strong knowledge in Probability and Statistics (e.g. MLE; CLT; hypothesis testing; Bayesian inference). Analytical Thinking, Problem-Solving Skills, Attention to Detail. Big Picture Vision, Strategic Thinking. Proactivity and Initiative, autonomy. Business Acumen: the ability More ❯

and Mr Green and we're looking for an experienced (London or Leeds based) Data Scientist to be responsible for developing AI/ML methods, processes and systems to extract knowledge or insights to drive the future of artificial intelligence. What you will be doing: Applying and/or … developing statistical modelling techniques (such as deep neural networks, Bayesian models, Generative AI, Forecasting), optimization methods and other ML techniques. Converting data into practical insights. Analysing and investigating data quality for identified data and communicate it to the Product Owner, Business Analyst, and other relevant stakeholders. More ❯

modelling, machine learning, and probability theory, preferably in the sports or gaming/betting industries. Familiarity with techniques such as Monte Carlo simulation, Bayesian modelling, mixed effects models, Kalman filters, GLMs, and time series forecasting. Strong programming skills, particularly in Python. Experience in exploring new datasets, identifying More ❯

modelling, machine learning, and probability theory, preferably in the sports or gaming/betting industries. Familiarity with techniques such as Monte Carlo simulation, Bayesian modelling, mixed effects models, Kalman filters, GLMs, and time series forecasting. Strong programming skills, particularly in Python. Experience in exploring new datasets, identifying More ❯

to grow and you to mentor. What you bring to the table Enthusiasm about using machine learning, especially deep learning and/or probabilistic methods, for science and engineering. Ability to scope and effectively deliver projects. Strong problem-solving skills and the ability to analyse issues, identify causes, and … applied statistics, mathematics, physics, engineering, or a related field, with particular expertise in any of the following: operator learning (neural operators), or other probabilistic methods for PDEs; geometric deep learning or other 3D computer vision methods for point-cloud or mesh-structured data; generative models for geometry and … spatiotemporal data (VAEs, Diffusion Models, Bayesian non-parametric, scaling to large datasets, etc.). Ideally, >2 years of experience in a data-driven role, with exposure to: building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., NumPy, SciPy, Pandas, PyTorch, JAX), especially More ❯

specialist systems, game theory, decision support systems, multi-agent systems Data fusion and state estimation/tracking algorithms e.g. Kalman Filtering, multiple-model tracking methods, particle filters, grid-based estimation methods, Multi-Object-Multi-Sensor Fusion, data-association, random finite sets, Bayesian belief networks, Dempster … Shafer theory of evidence Machine Learning for regression and pattern recognition/discovery problems e.g. Gaussian processes, latent variable methods, support vector machines, probabilistic/statistical models, neural networks, Bayesian inference, random-forests, novelty detection, clustering Deep Learning e.g. Deep reinforcement learning, Monte-Carlo tree search More ❯

and reporting as well as ensuring the availability of clean, structured, and reliable marketing data. Explore and implement advanced modelling techniques such as Bayesian MMM, hierarchical modelling, and causal inference. Present findings and insights to senior stakeholders, communicating complex concepts in a clear and actionable way. Stay … business impact. Demonstrated ability to present findings to both technical and non-technical audiences, with experience leading key technical projects. Preferred: Experience with Bayesian modeling, causal inference, and advanced statistical techniques. Prior experience in B2B marketing analytics. Prior experience in a consulting or marketing agency environment is More ❯

and reporting as well as ensuring the availability of clean, structured, and reliable marketing data. Explore and implement advanced modelling techniques such as Bayesian MMM, hierarchical modelling, and causal inference. Present findings and insights to senior stakeholders, communicating complex concepts in a clear and actionable way. Stay … business impact. Demonstrated ability to present findings to both technical and non-technical audiences, with experience leading key technical projects. Preferred: Experience with Bayesian modeling, causal inference, and advanced statistical techniques. Prior experience in B2B marketing analytics. Prior experience in a consulting or marketing agency environment is More ❯

and reporting as well as ensuring the availability of clean, structured, and reliable marketing data. Explore and implement advanced modelling techniques such as Bayesian MMM, hierarchical modelling, and causal inference. Present findings and insights to senior stakeholders, communicating complex concepts in a clear and actionable way. Stay … business impact. Demonstrated ability to present findings to both technical and non-technical audiences, with experience leading key technical projects. Preferred: Experience with Bayesian modeling, causal inference, and advanced statistical techniques. Prior experience in B2B marketing analytics. Prior experience in a consulting or marketing agency environment is More ❯

that drive the ToffeeX engineering design software. You will collaborate with a multidisciplinary team to solve complex engineering problems, focusing on mathematical optimization, numerical methods for partial differential equations (PDEs), and computational geometry. This role offers the opportunity to work at the intersection of advanced mathematics, engineering, and technology … solve unique real-world engineering problems. Essential Skills & Experience Ph.D. in Applied Mathematics, Physics, Engineering, or a closely related field. Research level experiencein numerical methods for PDEs, mathematical modelling or PDE-constrained optimization. Hands-on experience in developing and implementing numerical algorithms for solving complex simulation or optimization problems. … numerical optimization topics such as nonconvex, robust, or Bayesian optimization. Experience with topology or shape optimization. Experience with AI/ML methods for physics or optimization problems. Experience with software development in C++, Python, or similar languages. Company Benefits Flexibility and support for continuous learning and More ❯

About The Role As a Quantitative Analyst on our prediction team, you will use our extensive datasets to enhance existing predictive models, research new methods, and turn your insights into production-ready solutions. This research will involve a mix of well-executed analyses and innovative modelling to solve unique … challenges in football analytics, where traditional methods often need to be adapted or reinvented. To achieve this, you will have the freedom to explore and develop your own ideas while working collaboratively with a team of quants, developers, and analysts, to combine technical expertise with football knowledge. You will … predictive modelling, machine learning, and probability theory, preferably in sports or gaming/betting industries Familiarity with techniques such as Monte Carlo simulation, Bayesian modelling, mixed effects models, Kalman filters, GLMs, and time series forecasting. While expertise in every area isn't expected, you should have a More ❯

best practices for testing exercises. Developing our approach to uncertainty quantification and significance testing, increasing statistical power (given time and token constraints). Developing methods for inferring model capabilities across given domains from task or benchmark success rates, and assigning confidence levels to claims about capabilities. Predictive Evaluations: The … of machine learning, and statistical analysis (T-shaped: some deep knowledge, lots of shallow knowledge, in e.g. experimental design, A/B testing, Bayesian inference, model selection, hypothesis testing, significance testing). Deeply care about methodological and statistical rigor, balanced with pragmatism, and willingness to get into More ❯