26 to 41 of 41 Remote/Hybrid Bayesian Methods Jobs

producing strong quantitative evidence to inform strategy and improve outcomes for people and communities. You will develop predictive models, apply Bayesian methods, and analyse time-series data using Python or R and SQL, translating complex findings for non-technical colleagues in a hybrid, 4-days ...

delivery, and ensure actionable insights reach senior stakeholders and C-suite executives. You will build dashboards, apply econometrics and Bayesian methods, and translate complex results into practical recommendations, driving ROI and client success. Hybrid work with a central London base. #J-18808-Ljbffr ...

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 ...

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 ...

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 ...

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 ...

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 ...

array of statistical genetics approaches (e.g., GWAS, colocalization, fine-mapping, Mendelian randomization, polygenic risk scores)Experience defining phenotypes from electronic health records.Knowledge of Bayesian inference, high dimensional statistics, causal inference.Experience with software engineering practices (version control [Git & GitHub], testing, documentation, agentic coding, containerisation)Experience … building and running reproducible pipelines e.g., using WDL, Snakemake, or NextFlow.Experience using cloud computing and trusted research environments e.g. DNAnexus or Verily Workbench.Experience with methods development within statistical geneticsYour PackageWe are committed to providing a transparent, supportive, and rewarding work environment.Compensation & GrowthCompetitive Salary: Salaries are externally benchmarked annually ...

statistical genetics approaches (e.g., GWAS, colocalization, fine-mapping, Mendelian randomization, polygenic risk scores)Experience defining phenotypes from electronic health records. Knowledge of Bayesian inference, high dimensional statistics, causal inference. Experience with software engineering practices (version control [Git & GitHub], testing, documentation, agentic coding, containerisation)Experience building and … running reproducible pipelines e.g., using WDL, Snakemake, or NextFlow. Experience using cloud computing and trusted research environments e.g. DNAnexus or Verily Workbench. Experience with methods development within statistical geneticsYour PackageWe are committed to providing a transparent, supportive, and rewarding work environment. Compensation & GrowthCompetitive Salary: Salaries are externally benchmarked annually ...

hormonal rhythms observed across individuals. To characterise this variability, you will take advantage of unique in its kind datasets and advanced mathematical and computational methods and help develop personalised models. The practical element of your project will be based on, but not limited to, time series analysis, network analysis … being welcoming and supportive. The applicant should have high proficiency with scientific computer programming in Python, Julia, R and/or MATLAB, parameter estimation methods and HPC simulations. Experience with dynamical systems theory and mathematical modelling using ODEs and/or DDEs is desirable. The vacancy ...

delivery teams within the UK Defence or Aerospace sectors. Experience in the practical application of mathematical techniques such as Monte Carlo simulation or Bayesian inference. Experience working with MoD assessment frameworks and an understanding of the UK Defence Science and Technology (S&T) landscape. Experience conducting peer … production environment. Experience working within secure environments and an understanding of the constraints of working with classified information. Expert knowledge of numerical methods, optimisation, and statistical analysis within a defence context Proficiency in mathematical programming using Python (NumPy, SciPy), FORTRAN, or Julia. Familiarity with simulation tools and synthetic environments ...

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 You5+ 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 and collaborate ...

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 ...

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 ...