Data Scientist (Medical Imaging & AI) at Axle Informatics

Axle Informatics is seeking a Data Scientist with a strong background in medical imaging and AI to help turn clinical images into research-ready data and models. You will join a team that builds reproducible pipelines for preparing, segmenting, and analyzing medical images, and for linking imaging results to clinical data for machine learning.

This is a hands-on role. You will write and maintain Python, apply both AI and traditional image analysis methods to large imaging collections, and build and evaluate models that combine imaging with other clinical data. You will be expected to check results critically and catch problems before they become findings.

The work supports biomedical research programs with federal health partners. You will work in a highly interdisciplinary environment, where progress depends on understanding colleagues from other disciplines and helping them understand you. It values scientific rigor, reproducibility, well-tested code, and open collaboration. If you enjoy the full path from raw images to a validated model, and want that work to be usable by others, we would love to meet you.

Key Responsibilities:

• Medical Image Processing: Build and maintain Python pipelines that prepare large, varied medical imaging collections for analysis, with attention to metadata quality and inconsistencies found in real clinical data.
• AI for Medical Imaging: Develop, apply, and evaluate AI models for segmentation, classification, and image embeddings. Judge model quality and decide when a model needs retraining or a different approach.
• Traditional Image Analysis: Apply established image analysis methods for registration, normalization, and feature extraction, and combine them with machine learning where they fit the problem.
• Machine Learning and Multimodal Modeling: Build and evaluate models that combine imaging with clinical data and reports, using sound validation practices.
• Clinical Data Integration: Work with clinical data models and standard vocabularies to connect imaging results with patient records, cohorts, and outcomes.
• Reproducible, Scalable Workflows: Run pipelines with workflow orchestration, containers, and cluster or cloud computing. Keep code tested and documented so others can rerun the work.
• Collaboration and Communication: Work closely with clinicians, imaging scientists, and software engineers to define questions and interpret results. Present findings clearly, and contribute to publications, conference presentations, and open-source tools.

Required Qualifications:

• A Master’s degree or PhD in biomedical engineering, computer science, data science, medical physics, electrical engineering, or a related quantitative field, or a Bachelor’s degree with equivalent experience.
• Three or more years of hands-on experience in medical image analysis, in research or industry.
• Strong Python skills, including scientific computing and deep learning libraries, with a habit of writing tested, maintainable code.
• Familiarity with DICOM, NIfTI, and other medical image formats.
• Experience applying AI to medical images across segmentation, classification, and embeddings.
• Experience with traditional image analysis algorithms for registration, feature extraction, and machine learning.
• Experience evaluating models rigorously on imaging data, with sound validation design and appropriate performance metrics.
• Experience working with clinical imaging data and its privacy requirements.
• Comfort working in Linux environments with version control and containers.
• Collaboration & Communication: Ability to work effectively in a highly interdisciplinary environment with clinicians, imaging scientists, biologists, and software engineers, and to explain complex technical concepts clearly to people in other disciplines.

Preferred Qualifications:

• Experience with Nextflow or similar workflow orchestration tools on clusters or cloud platforms.
• Experience with EHR data, clinical data models such as OMOP or FHIR, and clinical vocabularies.
• Familiarity with the OMOP medical imaging extension, or with imaging data in research data warehouses.
• Experience with multimodal models that combine imaging with EHR data or clinical text, including medical foundation models.
• Experience with microscopy or digital pathology image analysis.
• Experience with SQL and relational or analytical databases.
• Track record of publications, conference presentations, or open-source contributions in medical imaging or machine learning.
• Experience working with NIH or other federal health and biomedical research organizations.