What are the responsibilities and job description for the Data Scientist - Biomedical Signal Processing position at Sphere?
Position: Data Scientist / Machine Learning Engineer
Client: AI-Driven HealthTech / Biosignal Analytics Product
Engagement Type: Consulting → Potential Phase 3 Implementation
Location: Remote
Level: Junior/Middle
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
Client: AI-Driven HealthTech / Biosignal Analytics Product
Engagement Type: Consulting → Potential Phase 3 Implementation
Location: Remote
Level: Junior/Middle
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
- 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
- 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
- Phase 1–2: Consulting / Advisory
- Phase 3: Model Training & Implementation
- Mid-level hands-on specialist
- Real-time biosignal AI product