GenAquarius - Health Data Scientist/ML Engineer

Key Responsibilities :

- Develop and optimize machine learning models using wearable and physiological sensor data

- Perform time-series modeling on health, wearable, and IoT-generated datasets

- Design and implement feature engineering pipelines for signals such as HRV, accelerometer, and SpO2 data

- Build and maintain data cleaning, annotation, and training data preparation pipelines

- Evaluate ML models using accuracy, calibration, fairness, and safety metrics

- Analyze physiological and behavioral data to derive actionable health insights

- Collaborate with AI, product, and engineering teams on healthcare-focused ML applications

- Support experimentation and validation of predictive health models

- Work on scalable data processing and model optimization workflows

- Ensure reliability, interpretability, and robustness of health-related ML systems

Required Skills & Experience :

- Strong experience in time-series modeling on wearable, sensor, or physiological datasets

- Expertise in feature engineering from signals such as :

- HRV (Heart Rate Variability)

- Accelerometer data

- SpO2 signals

- Experience building training data preparation, cleaning, and annotation pipelines

- Strong understanding of model evaluation techniques including :

- Accuracy

- Calibration

- Subpopulation fairness

- Safety metrics

- Strong Python, ML, and data science expertise

- Experience working with healthcare or sensor-driven datasets

- Excellent analytical and problem-solving skills

Nice to Have :

- Domain knowledge in :

- Sleep science

- Recovery analytics

- Glucose dynamics

- Metabolic health

- Experience working with public health datasets such as :

- MIMIC

- MESA

- UK Biobank

- Experience creating instruction-tuning datasets from domain-specific data sources

- Exposure to healthcare AI or wellness-focused ML systems