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 aData Scientist / ML Engineer with a biomedical signal processing background to support development ofreal-time AI solutions based on physiological signals.

The role begins withconsulting, followed byhands-on model training and optimization duringPhase 3 of product development.

The ideal candidate has experience working withmessy physiological datasets, includingECG, EEG, EOG, brain waves, or other low-frequency biosignals, and is comfortable buildingend-to-end ML pipelines — from signal filtering and feature engineering toreal-time model deployment.

Key Responsibilities

Phase 1–2: Consulting & Architecture

• Analyzephysiological signal datasets and data quality
• Recommendsignal preprocessing and filtering strategies
• Definefeature engineering approach for biosignals
• Suggestmodel architecture for real-time predictions
• Advise ondata pipeline and training strategy
• Help defineevaluation metrics and validation approach

Phase 3: Model Training & Implementation

• Processlow-frequency physiological signals (ECG, EEG, brain waves, biosignals)
• Applysignal filtering, noise reduction, and transformations
• Buildfeature extraction pipelines from physiological data
• Train and optimizemachine learning models
• Supportreal-time inference and model performance optimization
• Work closely with engineering team formodel integration
• Improve model accuracy throughexperimentation and iteration

Required Experience

• 2+ years experience asData Scientist / ML Engineer / Biomedical Data Scientist
• Strongsignal processing background
• Experience working withphysiological or biomedical signals such as:
  • ECG
  • EEG
  • EOG
  • Brain waves
  • Other biosignals
• Experience working withlow-frequency signals
• Experience handlingnoisy 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 withmulti-source physiological datasets
• Experience buildingreproducible research pipelines
• Experience withreal-time ML solutions
• PyTorch / TensorFlow experience

Engagement Model

• Phase 1–2: Consulting / Advisory
• Phase 3: Model Training & Implementation
• Real-time biosignal AI product