About Us

AccuLine is a medical device startup founded with the vision to save 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 & 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 and resources 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 Scientists & Data Engineer. In this role, you will develop machine learning models for the detection of coronary artery disease, as part of the core technology of CORA. In addition, you will be responsible for the development and maintenance of the data pipeline and data lifecycle, and the associated research tools.

Key Responsibilities:

• Feature engineering according to 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
• Establishment and maintenance of an automated data pipeline from raw data to model
• Establishment and maintenance of machine learning research management platform
• Collaboration with cross-functional teams, including clinical, regulatory and product team

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)
• Experience ML techniques for the complete life cycle of data analysis: preprocessing, feature engineering, feature extraction, classical ML model training and validation (using packages like Scikit-learn, AutoML packages, etc.)
• Experience with 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)
• 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
• Proficiency in setting up and maintaining data pipeline orchestration tools (such as Airflow, Dagster, Perfect)
• Proficiency in containerization technologies, particularly Docker, for developing, deploying, and scaling data science solutions.

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.)

Desired Qualities

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