Data Science · London / Remote · Full-time

RoleWe 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.You will work closely with AI engineers, medical advisors, and the product team to design models that operate in real-world environments with noisy and incomplete data.ResponsibilitiesDevelop Bayesian statistical models to estimate physiological deterioration risk from multi-sensor dataBuild probabilistic frameworks integrating vital signs, haemodynamic indicators, motion data, and environmental variablesDesign and run simulation studies to test triage algorithms and risk scoring systemsWork on uncertainty quantification and probabilistic inference for medical decision supportCollaborate with engineers to integrate statistical models into machine learning

pipelinesAnalyse physiological and operational datasets to identify predictive patternsSupport validation of models against real-world medical outcomesContribute to scientific publications and technical documentationProfilePhD or strong MSc in biostatistics, epidemiology, statistics, applied mathematics, or a related fieldExperience with Bayesian inference and probabilistic modellingFamiliarity with health data, physiological signals, or medical datasetsStrong programming skills in Python or RExperience with probabilistic frameworks such as PyMC, Stan, TensorFlow Probability, or similarAbility to work with noisy or incomplete real-world dataNice to HaveExperience with physiological monitoring, biosensors, or wearable devicesExperience with causal inference or survival analysisExperience working with clinical datasets or emergency medicineInterest in defence technology, emergency response, or austere medical environments #J-18808-Ljbffr