determined fraud attacks. Design and build high performance end to end systems spanning all parts of the identity verification application from data acquisition, data processing, and post processing. Mentor and work with junior scientists to research and deliver innovative technologies that utilize every bit of signal in the data. … Skills: Technical experience in uncertainty modelling, explainable machine learning techniques, out-of-distribution detection, life-long learning, and related fields. Technical experience with signal processing and imageprocessing techniques. Working knowledge of cloud computing (preferably AWS) and tools and technologies such as Kubernetes, Docker, Git, etc.. Where More ❯
london, south east england, United Kingdom Hybrid / WFH Options
Entrust
determined fraud attacks. Design and build high performance end to end systems spanning all parts of the identity verification application from data acquisition, data processing, and post processing. Mentor and work with junior scientists to research and deliver innovative technologies that utilize every bit of signal in the data. … Skills: Technical experience in uncertainty modelling, explainable machine learning techniques, out-of-distribution detection, life-long learning, and related fields. Technical experience with signal processing and imageprocessing techniques. Working knowledge of cloud computing (preferably AWS) and tools and technologies such as Kubernetes, Docker, Git, etc.. Where More ❯
construction, validation, and calibration. Experience with uncertainty quantification and performance estimation (e.g., cross-validation, bootstrapping, Bayesian credible intervals). Familiarity with database and data processing tools (e.g., SQL, MongoDB, Spark, Pandas). Ability to translate ambiguous business problems into structured, measurable, and data-driven approaches. Preferred Qualifications: M.Sc or … PhD in Statistics, Electrical Engineering, Computer Science, Physics, or a related field. Background in generative modeling , Bayesian deep learning , signal/imageprocessing , or graph models . Experience applying probabilistic models in real-world applications (e.g., recommendation systems, anomaly detection, personalized healthcare, etc.). Understanding of modern ML More ❯
