Remote job
Biostatistician – Bayesian Modelling
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About this role
Role overview Develop Bayesian models that use physiological and operational data to estimate the risk of serious patient deterioration and help prioritize triage. You’ll work across statistical research and practical implementation, collaborating with engineering, medical, and product colleagues to build and assess models for noisy, incomplete real-world data.
Responsibilities - Create Bayesian models to estimate deterioration risk from multiple sensor inputs. - Combine vital signs, haemodynamic indicators, motion measurements, and environmental variables in probabilistic frameworks. - Design and run simulations to test triage algorithms and risk-scoring methods. - Quantify uncertainty and develop probabilistic inference approaches for medical decision support. - Analyze physiological and operational datasets to identify predictive patterns. - Work with engineers to integrate statistical models into machine-learning pipelines. - Help validate models against real-world medical outcomes and contribute to technical documentation and scientific publications.
Requirements - PhD or strong MSc in biostatistics, epidemiology, statistics, applied mathematics, or a related discipline. - Experience with Bayesian inference and probabilistic modelling. - Familiarity with health data, physiological signals, or medical datasets. - Strong programming skills in Python or R. - Experience with a probabilistic framework such as PyMC, Stan, or TensorFlow Probability. - Experience designing simulation experiments or Monte Carlo studies, and working with noisy or incomplete data.
Nice to have - Experience with physiological monitoring, biosensors, or wearable devices. - Familiarity with causal inference, survival analysis, clinical data, or emergency medicine. - Interest in emergency response, austere medical environments, or defence-related applications.
Benefits and work setup - Full-time role with a London or remote work location listed; further details about remote eligibility and benefits are not provided.