Note: The job is a remote job and is open to candidates in USA. Sphere is an AI-Driven HealthTech company specializing in biosignal analytics. They are seeking a Data Scientist / Machine Learning Engineer with a biomedical signal processing background to support the development of real-time AI solutions based on physiological signals.
Responsibilities
- 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
- 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
Skills
- 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
- 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
Company Overview
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