7 of 7 Remote Bayesian Methods Jobs in London

stakeholders and translating research into 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, Torch or Caffe. The perks More ❯

junior team members, leading research 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 protein engineering. Understanding of protein More ❯

libraries like mplsoccer . Advanced MLOps & Modelling: Deeper experience with the Vertex AI lifecycle (especially Pipelines ) and advanced modelling techniques relevant to football (player valuation, tactical analysis). Bayesian Modelling: Experience with probabilistic programming (e.g., PyMC). Stakeholder Management: Proven success working directly with business stakeholders to define and deliver impactful solutions. What They Offer Work that More ❯

skills Track record shipping ML 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 We are a well treated bunch, with awesome benefits! If there's something important to you that's not on More ❯

core technologies Collaborate with data scientists and engineers to integrate models into pipelines and tools. Stay current with academic and industry advances in temporal modeling and deep learning. Document methods and support reproducibility, validation, and publication where appropriate. Essential Requirements Strong programming and modeling 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 field Ability to work in a research-style setting and translate ideas into More ❯

ALL the above requirements are met. What Will Make You Stand Out Advanced catastrophe modelling knowledge, especially in exposure/vulnerability domains Applied statistical skills in uncertainty quantification, Bayesian statistics, or Extreme Value Theory Machine learning experience related to climate risk or catastrophe modelling Experience with cloud platforms such as AWS or Google Cloud If you are … Risk Analyst, Loss Modeller, Statistical Modelling, Earth Observation, Python Programming, R Programming, Catastrophe Modelling, Hazard Modeller, Geospatial Data, Climate Data Science, Extreme Value Theory, Data Scientist, Machine Learning, Bayesian Statistics, Exposure Modelling, Vulnerability Analysis, Model Builder, Hybrid Working, Visa Sponsorship, Relocation Support, Climate Analytics, Financial Risk, Physical Risk, Research Scientist, Data Modelling More ❯

feedback, and a growth mindset. What You’ll Do Design, build, and scale machine learning models using environmental and observational data. Apply advanced causal inference techniques such as Bayesian Neural Networks, Gaussian Processes, Difference-in-Differences, and Synthetic Control methods. Leverage foundation models (e.g. Prithvi, Clay) and transformers to extract insights from complex datasets. Work cross-functionally … and environmental science, integrating relevant innovations into production. Mentor junior team members and foster best practices in applied ML. What You Bring Strong background in applied machine learning, bayesian statistics, and causal inference. Proficiency in Python and ML frameworks such as PyTorch. Experience with cloud infrastructure (e.g., AWS, GCP). A clear, concise communication style - clear examples More ❯