Bayesian Data Scientist

ZipRecruiter; London; On-site; GBP 60,000 - 100,000; Full time

Job summary

An innovative company is seeking a passionate Data Scientist to revolutionize healthcare through wearable technology. In this high-impact role, you will design and implement machine learning algorithms for real-time analysis of physiological signals, ensuring compliance with clinical-grade standards. Your contributions will shape the future of digital health, enhancing patient care and outcomes. If you have a strong background in machine learning and signal processing, along with a desire to make a meaningful impact in healthcare, this opportunity is perfect for you.

Qualifications

• 3-5+ Jahre Erfahrung in der Anwendung von Machine Learning auf Zeitreihendaten.
• Starke Kenntnisse in der Signalverarbeitung und Zeitreihenmodellierung.

Responsibilities

• Entwicklung von Machine Learning-Modellen zur Analyse von tragbaren Biosignaldaten.
• Zusammenarbeit mit klinischen und regulatorischen Teams zur Einhaltung von FDA-Anforderungen.

Skills

Machine Learning

Signal Processing

Time-Series Modelling

Python

MLOps

Education

MS in Machine Learning

PhD in Biomedical Engineering

PhD in Computer Science

Tools

PyTorch

TensorFlow

Job description

Job Description

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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