Bayesian Data Scientist

Our client is revolutionising healthcare for millions of patients worldwide. They are at the forefront of wearable technology, connecting patients and physicians with continuous, data-driven dialogue. This unique position offers daily directed guidance that redefines primary care while helping people live happier, healthier, and longer lives. They are looking for a passionate Data Scientist to develop impactful healthcare solutions using wearable data. This high-impact role provides an opportunity to shape the future of digital health and bring clinically validated, regulatory-ready Machine Learning solutions to market.

What you'll do:

As a Data Scientist, you will play a key role in building real-time, FDA-compliant algorithms that analyse continuous physiological signals from wearables. Your work will involve designing machine learning models for real-time analysis of wearable biosignal data such as ECG, PPG, accelerometer. You will also be responsible for developing algorithms that meet clinical-grade performance standards for use in regulated environments. Collaborating with various teams will be a significant part of your role to ensure solutions align with FDA, SaMD, and GMLP requirements.

• Design and implement machine learning models for real-time analysis of wearable biosignal data.
• Develop algorithms that meet clinical-grade performance standards for use in regulated environments.
• Preprocess and manage large-scale, continuous time-series datasets from wearable sensors.
• Collaborate with clinical, product, and regulatory teams to ensure solutions align with FDA, SaMD, and GMLP requirements.
• Optimize algorithms for deployment on resource-constrained devices.
• Run thorough validation experiments including performance metrics like sensitivity, specificity, ROC-AUC, and precision-recall.
• Contribute to technical documentation and regulatory submissions for medical-grade software.

What you bring:

The ideal candidate for this Data Scientist role will have an MS or PhD in Machine Learning, Biomedical Engineering, Computer Science or a related field. You should have 3–5+ years of experience applying machine learning to time-series or physiological data. A strong foundation in signal processing and time-series modelling is essential. Proficiency in Python and ML frameworks such as PyTorch or TensorFlow is required. Familiarity with FDA regulatory pathways for medical software is crucial.

• MS or PhD in Machine Learning, Biomedical Engineering, Computer Science, or a related field.
• 3–5+ years of experience applying machine learning to time-series or physiological data.
• Strong foundation in signal processing and time-series modelling.
• Proficiency in Python and ML frameworks such as PyTorch or TensorFlow.
• Familiarity with FDA regulatory pathways for medical software.
• Experience with MLOps practices and model versioning in compliant environments.

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