Research Assistant I - Radiation Oncology
Mass General Brigham
United States of America
Job posting number: #7365981 (Ref:RQ4070565)
Posted: June 25, 2026
Job Description
Mass General Brigham relies on a wide range of professionals, including doctors, nurses, business people, tech experts, researchers, and systems analysts to advance our mission. As a not-for-profit, we support patient care, research, teaching, and community service, striving to provide exceptional care. We believe that high-performing teams drive groundbreaking medical discoveries and invite all applicants to join us and experience what it means to be part of Mass General Brigham.
Job Summary
The Research Assistant works within the clinical research program and supports the research team in the overall conduct of clinical trials using Good Clinical Practice under the auspices of the Principal Investigator(s) and the DF/HCC Clinical Trials Office and/or MGB Institutional Review Board (IRB).The Research Assistant (RA) will be responsible for the primary data collection and management of patient clinical information as it pertains to participation in clinical trials. We are looking for a highly organized candidate with great attention to detail for the position of data coordinator. RAs are responsible for data entry, cleaning, transformations, and data analyses as requested by analysts or senior management.
Additionally, RAs will need to adhere to legal and regulatory standards, among other duties, requiring them to maintain excellent organizational skills and data integrity. For this role, the Research Assistant will support work from a faculty member focused on application of artificial intelligence and data science to optimize the care of cancer patients with a focus on lung cancer. The faculty member(s) is committed to training and inspiring the next generation of health care leaders and clinicians, working on important clinical and healthcare issues, and utilizing new technologies like AI to improve outcomes and reduce toxicity. We seek an energetic and organized, and detail-oriented individual to assist with a portfolio of data science research projects in the Department of Radiation Oncology. Candidates with backgrounds in data science and informatics and/or experience with programming languages are preferred.
Because the team builds and maintains its own AI models, data pipelines, and research web applications in-house, this role combines clinical research coordination with hands-on data science and software engineering. Candidates who are comfortable working in a Linux environment, using version control, containerizing and deploying applications, and applying best SWE and Data Science practices are especially encouraged to apply. Prior exposure to clinical/healthcare data, electronic health records, and Good Clinical Practice (GCP) is an asset.
Qualifications
1. The successful candidate will be able to work with faculty and research team members to accomplish, data compilation, data management, statistical programming, and other research related duties.
2. May assist the study start-up process from receipt of protocols through the Scientific Review Committee and IRB submissions, and site activation activities
3. May assist coordinating and management of clinical trials, interact with study participants as directed/required by the protocol and/or study team.
4. These duties and responsibilities include but are not limited to, the following:
Extracting data from electronic health records
Management of a large lung cancer patient database including regulatory considerations
Compiling and cleaning data from various sources as well as assessing the data quality; routine data maintenance
Statistical programming on large data sets (For example: analyzing data sets extracted from electronic health record databases)
Analysis and validation of empirical results
Participating in manuscript presentations with opportunities for co-authorship
Producing charts and tables for manuscripts and presentations
Analyzing pilot data to support the start-up of new projects
Onboarding new research collaborators
Querying and harmonizing data across multiple clinical, imaging, and research systems (e.g., the electronic health record, research data registries, and radiation-oncology treatment-planning systems)
Developing and maintaining reproducible, version-controlled data-science pipelines and analysis code (e.g., using Git/GitHub)
Training, running, and validating deep-learning models on GPU infrastructure, including models applied to medical imaging and digital biomarkers
Building, maintaining, and deploying research web applications across development, staging, and production environments
Containerizing applications and supporting deployment and release workflows
Maintaining clinical trial regulatory documentation and supporting protocol/staff onboarding and IRB submissions in accordance with Good Clinical Practice
Other duties as assigned
5. Project topics include, but are not limited to:
Impact of radiation therapy on cardiac function and cardiac complications
Quantifying patient health/frailty from medical imaging using AI
Deep learning-based analysis of thoracic CT imaging to predict survival outcomes
Automation of radiation therapy planning and risk prediction with AI
Quantification of biological age and health from facial photographs and other medical imaging using deep learning
Development of large language model (LLM) and retrieval-augmented (RAG) tools for clinical research and knowledge management
Building and maintaining web platforms for deploying AI biomarkers and supporting prospective data collection
Additional Job Details (if applicable)
Qualifications:
Education
Bachelor's Degree Science required
Can this role accept experience in lieu of a degree?
Yes
Experience
New grad with some relevant coursework 0-1 year preferred
- Careful attention to details
- Good organizational skills
- Must have working knowledge of Excel
- Must have working knowledge of SAS, Stata, Python, or R
- Must have working knowledge of statistical analysis and data science (running and interpreting regressions, data visualization)
- Demonstrated knowledge of and experience working with health care-related research
- Experience in quantitative research methods and analysis
Preferred Skills (Data Science & Software Engineering)
Proficiency with Python for data analysis and machine learning (e.g., pandas, NumPy, scikit-learn)
Experience with deep-learning frameworks (e.g., PyTorch or TensorFlow) and GPU-accelerated model training/inference
Comfort working in a Linux/Unix environment and on remote servers via the command line (SSH)
Experience with version control systems (Git / GitHub) for collaborative, reproducible code
Experience with containerization (Docker) and deploying applications across development, staging, and production environments
Experience building or maintaining web applications (e.g., Python web frameworks such as Streamlit, Flask, or Django; basic front-end familiarity)
Working knowledge of SQL and relational databases
Experience with electronic data capture and clinical research systems (e.g., REDCap) and with electronic health record / clinical data extraction
Familiarity with medical imaging data (e.g., DICOM, CT)
Familiarity with large language models (LLMs), retrieval-augmented generation (RAG), or other applied AI tooling
Familiarity with cloud or high-performance computing environments
Remote Type
Work Location
Scheduled Weekly Hours
Employee Type
Work Shift
Pay Range
$21.00 - $29.01/HourlyGrade
5EEO Statement:
Mass General Brigham Competency Framework
At Mass General Brigham, our competency framework defines what effective leadership “looks like” by specifying which behaviors are most critical for successful performance at each job level. The framework is comprised of ten competencies (half People-Focused, half Performance-Focused) and are defined by observable and measurable skills and behaviors that contribute to workplace effectiveness and career success. These competencies are used to evaluate performance, make hiring decisions, identify development needs, mobilize employees across our system, and establish a strong talent pipeline.


