Data Scientist - Biomedical Signal Processing

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