Senior Data Scientist

7 days ago

Bengaluru, Karnataka, India New Groyp Talentoj Full time ₹ 8,00,000 - ₹ 18,00,000 per year

Role Overview :

Were looking for a Senior Data Scientist with hands-on expertise in the end-to-end lifecycle of machine learning models, from problem formulation and data acquisition to model deployment and monitoring. You'll work at the intersection of biomedical signal processing, machine learning, and sleep science, helping transform raw sensor streams into accurate metrics like HR, RR, HRV, and sleep stages. You'll collaborate with our hardware, firmware, and ML teams to take these algorithms from Python embedded firmware production.

What You'll Do

Design and implement pre-processing pipelines for signal and time series data.

Build algorithms/models for heart rate, respiration rate, movement detection, and sleep stage estimation.

Develop robust algorithms for real-world noisy environments and validate them against reference datasets

Conduct feature engineering for sleep-related ML models (time/frequency/wavelet features).

Collaborate with firmware and cloud teams to integrate algorithms into on-device and cloud pipelines.

Contribute to internal signal quality, scoring, and annotation tools.

You Have

4–5 years of experience in signal processing/data science/biomedical sensing.

Solid foundation in digital signal processing (filters, FFT, IIR/FIR, wavelets).

Experience with image models(CNN) and sequence/time series models

Proficiency in Python (NumPy, SciPy, neurokit2, pandas, matplotlib, scikit-learn)

Experience with sleep staging, physiological data analysis, or similar time-series modeling.

Comfort with real-world data: noise, motion artifacts, gaps, resampling, and validation.

(Bonus) Experience with embedded signal processing, PyTorch/TF for physiological ML, or on-device inference.

Nice to Have

Prior work with wearables, health tech, or medical devices (Ultrahuman, Dozee, Fourth Frontier, Muse, Fitbit).

Familiarity with BCG datasets, wavelet-based HR/RR detection, or open-source sleep datasets (PhysioNet, ISRUC, Sleep-EDF).

Understanding of biosignal labeling, ML evaluation, deployment and monitoring

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