12 of 12 Permanent Bayesian Methods Jobs in London

market and product data to support biopharma commercial product launches, and complex quant and qual research projects. This role is also responsible for GenerativeAI product development, applying complex statistical methods to a range of pharmaceutical market research data and utilizing rigorous testing methods. Statistical analysis/modelling of multiple data sources: research design (e.g. experiment/survey), data pre … based analytics/statistical project support role Proficient Analytics Toolkit: R, SPSS, Excel, Git, Python, SQL, Postgres & cloud computing expertise desirable. Consistently able to apply a range of research methods and demonstrate honed analytics skills Strong communication skills and interpersonal skills Solid project management skills Demonstrated experience building and maintaining client relationships Commercially focused mindset Coaching, Leadership and Management … experience Technical skillset: Segmentation & Discriminant analysis & tools (essential) Predictive modelling (e.g. logistic regression) (essential) Choice/Allocation based models (essential) Decision/Regression based trees (essential) Key Driver analysis methods (essential) Multivariate analysis (essential) Machine learning/AI methods (essential) Experimental design (desirable) Bayesian methods (desirable) Time series analytics (desirable) Text analytics (desirable) Data More ❯

in the AI Lab. Responsibilities Develop new ML models and AI techniques Lead on research projects within a global team Review relevant AI/ML literature to identify emerging methods, technologies, and best practices Explore new data sources and discover techniques for best leveraging data Minimum Qualifications A Master's or PhD in a field related to AI/… disciplines Strong background applying Deep Learning techniques (including implementing custom architectures, optimizing model performance, developing novel loss functions, metrics, and benchmarks, and deploying production-ready solutions) Familiarity in statistical methods for Machine Learning (e.g. Bayesian methods, HMMs, graphical models, dimension reduction, clustering, classification, regression techniques, etc.) Familiarity with PyTorch, PyTorch Lightning, or similar frameworks Strong More ❯

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 ❯

technical audiences. A self-starter who thrives in a fast-paced R&D environment. Experience in catastrophe modelling, especially around exposure and vulnerability. Applied statistical skills such as Bayesian statistics, uncertainty quantification, or Extreme Value Theory. Knowledge of machine learning applications in climate risk. Familiarity with cloud platforms (e.g., AWS, Google Cloud). If this role looks More ❯

and other databases to extract the data and build data pipeline Experience with Power BI or other data visualization tools especially geo coded data is a big plus Bayesian statistics, propensity modelling also a big plus Write POVs on industry topicsand provide thought leadershipon data privacy laws, third-party measurement tools, and space, the consumer marketplace, vertical 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 ❯

developed front-end componentry Interested in shaping the backend Python API or becoming involved in full stack development. Desired/Obtained Qualifications and experience: Some general knowledge of Bayesian networks or probability would be helpful Experience working closely with data scientists and other stakeholders, not just developers, and be comfortable demoing their work The product will be 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 ❯