Senior Data Scientist, Medical Imaging at HeartFlow in San Francisco, California

Job description

• 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.

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.
• Data Curation and Annotation: Develop annotation protocols for algorithm training and validation for internal product development and FDA submissions.
• Cross-Functional Collaboration and Communication: Partner with Research Scientists, Machine Learning Engineers, Systems Engineers, Process Engineers, Product and Regulatory teams. Derive and present clear analyses from messy data to drive decision-making, and provide artifacts other functions consume.

Requirements

• Education: Masters or PhD Degree in Data Science, Computer Science, Medical Image Analysis, Statistics, Biomedical Engineering, or a 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 the judgement to separate correctable bias from irreducible variance.
• Medical Imaging Expertise: Deep understanding of medical image data structures and the 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 (e.g., pandas, scipy, statsmodels, seaborn/matplotlib). Proven ability with large, complex, and messy datasets.
• Deep Learning Experience: Hands-on experience developing or fine-tuning deep learning algorithms (preferably using PyTorch) for computer vision tasks (segmentation, classification, detection) applied to medical images.
• AI-Augmented Workflow: Demonstrated proficiency using modern agentic tools and LLMs (e.g., GitHub Copilot, Gemini, Claude) as a daily force multiplier 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.
• Published research specifically related to domain generalization, image harmonization, or out-of-distribution (OOD) detection in medical imaging.
• Experience working with large multi-vendor CT imaging datasets, with an understanding of acquisition and reconstruction protocols.
• Proven track-record in diagnosing data- and annotation-related algorithm performance gaps, and designing data-driven solutions.
• Experience with large-scale data querying and cloud storage (e.g., AWS, SQL).
• Experience developing SaMD products and contributing to regulatory filings.
• Experience with biostats to support FDA submissions.

Skills

Required

• Python
• pandas
• scipy
• statsmodels
• seaborn/matplotlib
• PyTorch
• GitHub Copilot
• Gemini
• Claude
• AWS
• SQL

Benefits

• A reasonable estimate of the base salary compensation range is $170,000 to $240,000, bonus, and equity. #LI-IB1 #LI-Hybrid