Data Scientist

About Us

AccuLine is a medical device startup founded with the vision of saving millions of lives worldwide by preventing the next heart attack. AccuLine is developing CORA, an accurate and user-friendly system for the early detection of coronary artery disease, aiming to replace stress test exams. CORA comprises a console, ECG and SpO2 sensors, and cloud-based ML/DL analysis of these bio-signal measurements.

AccuLine has received grants from the Israeli Innovation Authority and Google, and among our investors are eHealth Ventures, Maccabi Healthcare Services, and Mayo Clinic.

This is a unique opportunity to join the company at its early stages, where you will be entrusted with the crucial task to shape the creation of a game-changing product with the potential to positively impact millions of lives.

Position Overview

AccuLine is seeking an experienced and driven individual to join our team as a Data Scientist. In this role, you will develop ML/DL models for the detection of coronary artery disease as part of the core technology of CORA.

Key Responsibilities

• Feature engineering based on existing literature and internal research efforts.
• Development of ML/DL classifiers for CAD detection and prediction from time-series biological data, according to well-defined business goals.
• Participate in the initiation and maintenance of the complete data pipeline.
• Collaborate with cross-functional teams, including clinical, regulatory, and product teams.

Necessary Qualifications

• A PhD or MSc in Computer Science, Electrical Engineering, Data Science, or a related field.
• 3+ years of proven coding experience (ideally in Python).
• Strong expertise in ML techniques for the complete lifecycle of data analysis: preprocessing, feature engineering, feature extraction, classical ML model training, and validation (using packages like Scikit-learn, AutoML packages, etc.).
• Strong expertise in DL architectures (such as CNN, RNN, foundation models) and frameworks (such as PyTorch and TensorFlow), specifically for time-series data.
• Hands-on experience with electrophysiological signals (ECG, EEG, EMG).

Strong Advantages

• In-depth knowledge of DSP techniques for biological signals (specifically ECG, SpO2, respiratory).
• Familiarity with data science metrics for clinical domains (sensitivity, specificity, etc.).
• Experience with AWS or other cloud platforms for data management, model training, and deployment.
• Proficiency in using data science management tools such as MLFlow, DVC, or ClearML.
• Experience in containerization of data pipelines.

Desired Qualities

• Ability to work collaboratively with a “can-do” attitude.
• Highly organized, detail-oriented, and analytical.
• Multi-tasking capabilities.
• Self-motivated.