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Data Scientist - Biomedical Signal Processing
Sphere Software
- United States
- United States
About
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
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Languages
- English
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