Senior Data Scientist

16 hours ago


bangalore, India Talentoj Full time

We're looking for a Senior Data Scientist with hands-on expertise in the end-to-end lifecycleof machine learning models, from problem formulation and data acquisition to modeldeployment and monitoring. You'll work at the intersection of biomedical signal processing,machine learning, and sleep science, helping transform raw sensor streams into accuratemetrics like HR, RR, HRV, and sleep stages.You'll collaborate with our hardware, firmware, and ML teams to take these algorithms fromPython → 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, andsleep stage estimation.● Develop robust algorithms for real-world noisy environments and validate them againstreference datasets● Conduct feature engineering for sleep-related ML models (time/frequency/waveletfeatures).● Collaborate with firmware and cloud teams to integrate algorithms into on-device andcloud pipelines.● Contribute to internal signal quality, scoring, and annotation tools.You Have● 4–8 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, mne, neurokit2, pandas, matplotlib,sckit-learn)● Experience with sleep staging, physiological data analysis, or similar time-seriesmodeling.● Comfort with real-world data: noise, motion artifacts, gaps, resampling, and validation.● (Bonus) Experience with embedded signal processing, PyTorch/TF for physiologicalML, 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-sourcesleep datasets (PhysioNet, ISRUC, Sleep-EDF).● Understanding of biosignal labeling, ML evaluation, deployment and monitoring



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