Imaging Data Scientist III

SHIFT:

Day (United States of America)

Seeking Breakthrough Makers

Children’s Hospital of Philadelphia (CHOP) offers countless ways to change lives. Our diverse community of more than 20,000 Breakthrough Makers will inspire you to pursue passions, develop expertise, and drive innovation.

At CHOP, your experience is valued; your voice is heard; and your contributions make a difference for patients and families. Join us as we build on our promise to advance pediatric care—and your career.

CHOP does not discriminate on the basis of race, color, sex, national origin, religion, or any other legally protected categories in any employment, training, or vendor decisions or programs. CHOP recognizes the critical importance of a workforce rich in varied backgrounds and experiences and engages in ongoing efforts to achieve that through equally varied and non-discriminatory means.

A Brief Overview

We are seeking an Imaging Data Scientist III to lead development of scalable imaging pipelines, infrastructure, and computational workflows for large-scale multi-institutional pediatric neuro-oncology and translational imaging studies. This position will support cutting-edge artificial intelligence (AI), machine learning (ML), and multimodal imaging research focused on improving diagnosis, risk stratification, treatment response assessment, and clinical decision support for pediatric brain tumors.

The successful candidate will work closely with multidisciplinary teams of clinicians, imaging scientists, data scientists, software engineers, and computational biologists to design and implement robust imaging analytics infrastructure capable of supporting large-scale collaborative research initiatives and clinical translation.

What you will do

• Lead development and maintenance of scalable, cloud-enabled imaging pipelines and computational infrastructure for multi-institutional imaging studies.
• Design and implement automated workflows for medical imaging data ingestion, harmonization, preprocessing, quality control, storage, and analytics.
• Develop infrastructure for management and analysis of large-scale multimodal datasets, including MRI, digital pathology, clinical, and molecular data.
• Collaborate with investigators and consortium partners to support standardized imaging analysis across diverse institutions and datasets.
• Build reproducible and containerized workflows using modern software engineering and MLOps practices.
• Support development, deployment, and optimization of AI and machine learning models for medical imaging applications. 
• Contribute to technical platform development for cloud-based and high-performance computing environments. 
• Assist with integration of imaging-derived biomarkers with clinical, genomic, and other multimodal data for predictive modeling.
• Mentor junior analysts, trainees, and research staff in imaging analytics and computational workflow development.
• Contribute to grant applications, manuscripts, technical documentation, and collaborative scientific initiatives.

Education Qualifications

• Master’s degree or PhD in Biomedical Engineering, Computer Science, Data Science, Medical Imaging, Bioinformatics, or a related quantitative field. Required
• PhD in Biomedical Engineering, Computer Science, Data Science, Medical Imaging, Bioinformatics, or a related quantitative field Preferred

Experience Qualifications

• At least five (5) years of of applied algorithm development, data science, applied statistics, machine learning, or mathematical modeling projects experience  Required
• Significant experience developing computational pipelines and infrastructure for medical imaging analysis.
• Strong programming skills in Python and/or related scientific computing languages.
• Experience working with medical imaging formats and frameworks (e.g., DICOM, NIfTI, MONAI, ITK, SimpleITK, NiBabel).
• Experience with cloud computing platforms, containerization, and workflow orchestration technologies (e.g., Docker, AWS, Airflow, or similar).
• Familiarity with machine learning and deep learning methods for imaging analysis.
• Experience working with large-scale, multi-institutional biomedical datasets.
• Experience in pediatric neuro-oncology or neuroimaging research. Preferred
• Experience with imaging harmonization, federated learning, or distributed data infrastructures. Preferred
• Familiarity with multimodal AI frameworks integrating imaging, clinical, and molecular data. Preferred
• Experience with MLOps, software engineering best practices, and deployment of research tools into production environments. Preferred
• Prior leadership or team