Data Scientist - Biomedical Signal Processing

Position Data Scientist / Machine Learning Engineer Client AI-Driven HealthTech / Biosignal Analytics Product Engagement Type Consulting → Potential Phase 3 Implementation Location Remote Role Overview We are looking for a Data Scientist / ML Engineer with a biomedical signal processing background to support development of real-time AI solutions based on physiological signals. The role begins with consulting, followed by hands‑on model training and optimization during Phase 3 of product development. The ideal candidate has experience working with messy physiological datasets, including ECG, EEG, EOG, brain waves, or other low-frequency biosignals, and is comfortable building end‑to‑end ML pipelines — from signal filtering and feature engineering to real‑time model deployment. Key Responsibilities Phase 1–2: Consulting & Architecture Analyze physiological signal datasets and data quality Recommend signal preprocessing and filtering strategies Define feature engineering approach for biosignals Suggest model architecture for real‑time predictions Advise on data pipeline and training strategy Help define evaluation metrics and validation approach Phase 3: Model Training & Implementation Process low-frequency physiological signals (ECG, EEG, brain waves, biosignals) Apply signal filtering, noise reduction, and transformations Build feature extraction pipelines from physiological data Train and optimize machine learning models Support real‑time inference and model performance optimization Work closely with engineering team for model integration Improve model accuracy through experimentation and iteration Required Experience 2+ years experience as Data Scientist / ML Engineer / Biomedical Data Scientist Strong signal processing background Experience working with physiological or biomedical signals such as: ECG EEG EOG Brain waves Other biosignals Experience working with low-frequency signals Experience handling noisy or heterogeneous physiological datasets Hands‑on experience with: Signal filtering Mathematical filters Feature extraction Time‑series analysis Python skills: NumPy SciPy Pandas Scikit-learn Nice to Have Biomedical engineering background Neuroimaging or electrophysiology experience Experience working with multi‑source physiological datasets Experience building reproducible research pipelines Experience with real‑time ML solutions PyTorch / TensorFlow experience Engagement Model Phase 1–2: Consulting / Advisory Phase 3: Model Training & Implementation Real‑time biosignal AI product #J-18808-Ljbffr Sphere