4 of 4 Remote Permanent Probabilistic Modelling Jobs

ML frameworks (e.g. PyTorch, TensorFlow, scikit-learn) Expertise in one or more of the following areas : o Multi-modal data fusion or time series analysis o Uncertainty quantification or probabilistic modelling o Active learning, explainable AI, or online learning o Geospatial data analysis or sensor-based modelling • Experience preparing technical deliverables and engaging with stakeholders Why Apply More ❯

experience with ML frameworks (e.g. PyTorch, TensorFlow, scikit-learn) Expertise in one or more of the following areas: Multi-modal data fusion or time series analysis Uncertainty quantification or probabilistic modelling Active learning, explainable AI, or online learning Geospatial data analysis or sensor-based modelling Experience preparing technical deliverables and engaging with stakeholders Eligible for SC Clearance More ❯

daily directed guidance stands to redefine primary care while helping people live happier, healthier, and longer. Job Summary: We're seeking a Bayesian Data Scientist with deep expertise in probabilistic modeling and a strong grasp of modern AI advancements, including foundation models , generative AI , and variational inference . This role is perfect for someone who thrives on solving complex … driving real business impact. Location: Remote/Hybrid/USA-SF, USA-remote, UK-London, UK-remote Responsibilities: Translate predictive modeling problems and business constraints into robust Bayesian or probabilistic AI solutions. Design and implement reusable libraries of predictive features and probabilistic representations for diverse ML tasks. Build and optimize tools for scalable probabilistic inference under memory … learning analyses, simulations, and experimental design. Stay current with emerging trends in generative modeling, causality, 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 More ❯

skills in Python (NumPy, PyTorch or TensorFlow, SciPy, pandas). In-depth knowledge of machine learning for time-series, including RNNs, LSTMs, GRUs, transformers, attention mechanisms. Solid understanding of probabilistic models (HMMs, Bayesian inference, graphical models). Experience designing or adapting dynamic programming algorithms. A graduate degree (PhD or MSc) in Computer Science, Mathematics, Physics, Bioinformatics, or a related More ❯