Please ‼️ ONLY APPLY ‼️ if you have worked with ECG (electrocardiogram) signal processing and use AI tools on a daily basis.

Remote position - EU time zone.

Job Summary:

We're hiring a Data Scientist to advance the algorithms behind our ECG analysis platform. You'll sharpen arrhythmia classification, ECG interval detection (PR, QRS, QT), and signal quality — choosing the right technique for each problem, from classical signal-processing and delineation through to machine learning.

If you bring deep, versatile biosignal-processing expertise and enjoy problems where the data doesn't arrive clean, we'd love to talk.

The ideal candidate will have experience in ECG signal processing, machine learning model development , and AI-driven diagnostics in healthcare applications.

Key Responsibilities:

- Improve and extend the arrhythmia-classification algorithms, raising accuracy and robustness on messy, real-world recordings.
- Advance ECG interval detection (PR, QRS, QT/QTc, RR) across resting ECG, selecting the right method for each problem rather than forcing one approach.
- Strengthen signal quality through denoising, baseline-wander removal, and artifact suppression.
- Build delineation and interval-measurement methods that hold up with little or no labelled data, and design sound strategies to validate them.
- Define and run algorithm evaluation against clinical ground truth — reporting classification metrics and interval-level error, and iterating to clinical-grade performance.
- Partner with the ECG technician and annotation team to specify, review, and improve labelling workflows and the quality of ground-truth data.
- Document algorithms, datasets,



and experiments to support quality and regulatory requirements.

Required skills (must-have):

- Strong Python and the scientific stack (NumPy, SciPy, pandas, scikit-learn).
- Broad, hands-on command of biosignal processing for ECG — not limited to any single family. This spans wavelet transforms (DWT / SWT / CWT / wavelet packets), derivative- and filter-based delineation (e.g., Pan-Tompkins-style QRS detection), template matching, and Hilbert-transform and other time-frequency methods — plus the judgement to choose the right technique for each problem.
- Experience building ECG delineation and interval-measurement that works with little or no labelled data — classical, rule-based, or model-based methods that need no training labels, together with sound strategies for validating them.
- Practical approaches to label scarcity: semi-supervised, self-supervised, and weakly-supervised learning, transfer learning from publicly annotated datasets, and designing annotation pipelines with clinical experts to create ground truth where none exists.
- Familiarity with the limited publicly annotated ECG resources for delineation and intervals (e.g., PhysioNet QT Database, LUDB, PTB-XL) and a clear understanding of their limitations.
- Deep learning with PyTorch and/or TensorFlow/Keras for biosignals (1-D CNNs, RNN / LSTM, transformers),



where labelled data supports it.
- Demonstrated experience working with ECG or other biosignal data.
- Sound evaluation discipline for imbalanced and limited clinical data — sensitivity, specificity, PPV, F1, and AUC for classification, plus interval-level error metrics (e.g., mean and SD of onset/offset deviation against a reference) — and an understanding of why these matter in a medical context.
- Version control (Git) and reproducible, well-documented experiment workflows.
- Comfortable working alongside AI assistants such as Claude as part of a modern development workflow — using them to accelerate prototyping, coding, and research while applying sound judgement to their output.

Preferred (nice-to-have):

- Domain knowledge of ECG morphology, fiducial points (P, QRS, T), and clinical intervals.
- Experience with probabilistic sequence models for delineation (e.g., hidden Markov models) and other model-based approaches that perform well with limited data.
- Familiarity with further DSP and decomposition methods (e.g., empirical mode decomposition, adaptive filtering) to draw on alongside the core toolkit.
- Experience with heart-rate-variability (HRV) analysis.
- Exposure to MLOps and deploying algorithms into production, on-device, or edge environments.
- Awareness of medical-device software standards and regulation (ISO 13485, IEC 62304, CE / MDR), including the handling of SOUP components.
- Experience collaborating with clinical annotators and clinicians.

This is a full time role, but other options (half-time, advisory, hourly) could be reviewed if the profile is a perfect match.

📌 Data Scientist – ECG Signal Processing & AI Development (India)
🏢 Cardiolyse
📍 India