Intermediate Data Management Specialist, Oncology

This position is for an Intermediate Data Management Specialist in Oncology, focusing on developing reproducible coding pipelines for medical image data analysis in lung cancer screening research. Key responsibilities include data pipeline development, programming, and applying machine learning techniques to medical images. A Bachelor's degree in Computer Science or a related field, programming experience (Python, R), and data management skills are required.

• Coding for existing data pipelines or de novo programming for programs and applications will be performed daily
• Regular reporting of progress and process to the supervisors and to the team will be required
• Developing data management pipelines
• Helping with technical writing for grant applications and reporting
• Developing and managing data application tools
• Working with principal investigators to develop, document and disseminate data processes
• Assisting with reporting of processes and pipelines for data management
• Conducting basic and advanced statistical analyses
• Programming and/or implementing new statistical and machine learning methods
• Conducting data quality checks as well as making and logging corrections
• Documenting data analysis steps and program code
• Preparing written summaries of data analyses that include figures and tables, as needed for scientific publications and presentations
• Designing, building, and maintaining data pipelines
• Documentation and communication of processes via various tools and software is essential
• Advanced data processing and data management skills will be required. Independent judgment, attention to detail, ability to learn new methods and strong problem-solving skills will be essential in performing the job duties
• Additional data responsibilities include: Developing data management pipelines Helping with technical writing for grant applications and reporting Developing and managing data application tools Working with principal investigators to develop, document and disseminate data processes Develop and apply machine learning (ML) approaches for health outcome prediction and risk assessment Application of commercial and open-source AI tools to medical images for various tasks including segmentation, feature extraction, risk assessment, classification, etc.
• Manipulating large and complex data sets, including multi-model medical images (e.g., CT, CXR, etc.) Assisting with reporting of processes and pipelines for data management Conducting basic and advanced statistical analyses Programming and/or implementing new statistical and machine learning methods Conducting data quality checks as well as making and logging corrections Documenting data analysis steps and program code Preparing written summaries of data analyses that include figures and tables, as needed for scientific publications and presentations
• Develop and apply machine learning (ML) approaches for health outcome prediction and risk assessment
• Application of commercial and open-source AI tools to medical images for various tasks including segmentation, feature extraction, risk assessment, classification, etc.
• Manipulating large and complex data sets, including multi-model medical images (e.g., CT, CXR, etc.)

• A minimum of B.Sc. degree in Computer Science, Data Science, or related discipline. An M.Sc. degree is preferred
• Excellent problem-solving skills and experience with programming (e.g., Python, R)
• Experience with both tabular (e.g., CSV, delimited, Excel) and non-tabular (e.g., JSON, XML) data structures, including wrangling non-tabular data into tabular data
• Experience with, or interest in, learning new skills in working with medical images (e.g., CT) and common storage formats (e.g., DICOM, Nifti, NRRD)
• Experience in database management (e.g., SQL, Arrow)
• Demonstrated ability to work with large, complex data sets stored in various databases
• Minimum one year experience applying data management approaches in a health workplace setting
• Excellent communication skills including advanced written and spoken English skills
• Ability to work collaboratively with members of an interdisciplinary team
• Knowledge and experience with epidemiologic, biostatistical, and clinical studies is an asset
• Experience with, or interest in, gaining skills in data science is an asset
• Application Deadline: October 20, 2026