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Salary: USD 170,000 - 240,000 / annual
HeartFlow is a publicly traded medical technology company (HTFL) advancing the diagnosis and management of coronary artery disease using AI-driven solutions. The flagship product, HeartFlow FFR_CT Analysis, is a non-invasive cardiac test that provides 3D models of coronary arteries to help clinicians assess blood flow and identify stenoses. The company's product suite includes RoadMap Analysis for stenosis identification and Plaque Analysis for atherosclerosis characterization. Used for over 750,000 patients worldwide, HeartFlow is cleared in the US, UK, Europe, Japan, and Canada.
In this Senior Data Scientist role, you will drive data-centric AI initiatives focused on understanding how real-world imaging conditions and technical variables affect image appearance and the accuracy of deep learning algorithms. You will own the analytical pipeline to quantify these effects and explore data-driven methods to address them, working cross-functionally with Research Scientists, Machine Learning Engineers, Systems Engineers, Product, and Regulatory teams.
Key responsibilities include:
- Building robust, reproducible analytical pipelines and visualizations over population-scale data to characterize dataset distributions and model vulnerabilities across diverse patient populations
- Exploring, developing, and validating methods for harmonizing complex data-related factors and image variations
- Translating findings about data variance and model behavior into actionable data curation requirements, training-time robustness strategies, and architectural recommendations for downstream algorithm development
- Developing annotation protocols for algorithm training and validation for internal product development and FDA submissions
- Partnering cross-functionally to derive and present clear analyses from messy data to drive decision-making and provide artifacts for other functions
You will be drawn to deriving insight from large and messy real-world imaging data and communicating solutions from these insights.
REQUIREMENTS:
- Education: Master's or PhD in Data Science, Computer Science, Medical Image Analysis, Statistics, Biomedical Engineering, or related quantitative field
- Experience: 5+ years (or 3+ with a PhD) of industry experience in Data Science, Machine Learning, or Image Analysis
- Measurement Science: Working command of reproducibility and agreement statistics (variance components, Gage R&R, intraclass correlation, repeatability and reproducibility coefficients, Bland-Altman) and judgment to separate correctable bias from irreducible variance
- Medical Imaging Expertise: Deep understanding of medical image data structures and physical/clinical realities of imaging; familiarity with image processing tools and building algorithms for medical imaging data
- Data Analysis & Statistics: Expert proficiency in Python and statistical data analysis ecosystems (pandas, scipy, statsmodels, seaborn/matplotlib); proven ability with large, complex, messy datasets
- Deep Learning Experience: Hands-on experience developing or fine-tuning deep learning algorithms (preferably PyTorch) for computer vision tasks (segmentation, classification, detection) applied to medical images
- AI-Augmented Workflow: Demonstrated proficiency using modern agentic tools and LLMs (GitHub Copilot, Gemini, Claude) as daily force multipliers to accelerate software development, rapidly prototype data solutions, and build reproducible pipelines
- Communication: Exceptional ability to distill complex, multi-dimensional data analyses into clear, strategic insights for cross-functional stakeholders
Preferred qualifications include published research in domain generalization, image harmonization, or out-of-distribution detection in medical imaging; experience with large multi-vendor CT imaging datasets; proven track record diagnosing data- and annotation-related algorithm performance gaps; experience with large-scale data querying and cloud storage (AWS, SQL); experience developing SaMD products and contributing to regulatory filings; and experience with biostats to support FDA submissions.
