Data Scientist

Heartflow is a medical technology company advancing the diagnosis and management of coronary artery disease, the #1 cause of death worldwide, using cutting-edge technology. The flagship product—an AI-driven, non-invasive cardiac test supported by the ACC/AHA Chest Pain Guidelines called the Heartflow FFRCT Analysis—provides a color-coded, 3D model of a patient’s coronary arteries indicating the impact blockages have on blood flow to the heart. Heartflow is the first AI-driven non-invasive integrated heart care solution across the CCTA pathway that helps clinicians identify stenoses in the coronary arteries (RoadMap™Analysis), assess coronary blood flow (FFRCT Analysis), and characterize and quantify coronary atherosclerosis (Plaque Analysis). Our pipeline of products is growing and so is our team; join us in helping to revolutionize precision heartcare.

Heartflow is a publicly traded company (HTFL) that has received international recognition for exceptional strides in healthcare innovation, is supported by medical societies around the world, cleared for use in the US, UK, Europe, Japan and Canada, and has been used for more than 750,000 patients worldwide.  

We are looking for a Senior Data Scientist with a strong foundation in both statistical data analysis and deep learning to drive our data-centric AI initiatives. In this role, you will be at the forefront of understanding how diverse real-world imaging conditions and technical variables affect image appearance and, in turn, the accuracy and reproducibility of deep learning algorithms.

You will own the analytical pipeline to quantify these effects and explore data-driven methods to address them. You will work cross-functionally to translate these insights into downstream algorithm development and product decision-making, including development of annotation protocols to curate high quality annotations for training and validation. If you are drawn to deriving insight from large and messy real-world imaging data, and to communicating and crystalizing solutions from these insights, this is the role for you.

Key Responsibilities

• Tooling & Pipelines: Build robust, reproducible analytical pipelines and visualizations over population-scale data, to characterise dataset distributions and model vulnerabilities across diverse patient populations.
• Correction & Harmonization: Explore, develop, and validate methods for harmonizing complex data-related factors and image variations. 
• Data-Centric Deep Learning: Translate findings about data variance and model behaviour into actionable data curation requirements, training-time robustness strategies, and architectural recommendations for downstream algorithm development.
• : Develop annotation protocols for algorithm training and validation for internal product development and FDA submissions.