Research Assistant , Trauma Imaging, Radiology

Site:
The Brigham and Women’s Hospital, Inc.

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.

The Trauma Imaging Research and Innovation Center (TIRIC) at Brigham and Women’s Hospital seeks a full-time Research Assistant to join a multidisciplinary team working at the intersection of clinical care, research, and innovation. TIRIC conducts high-impact, real-world research at the intersection of machine learning, large-scale clinical data, medical imaging, and public health, with close collaboration across Mass General Brigham (MGB), MIT, and select external academic collaborators.

This individual will serve as the primary Research Assistant for TIRIC and will be central to the day-to-day operations of the center, supporting multiple active projects simultaneously and helping maintain continuity across research, regulatory, and administrative workflows.

This role is centered on coordinating complex, multi-stakeholder research projects, supporting clinical studies, and facilitating collaboration across MGB, MIT, and clinical departments. The Research Assistant will play a key role in ensuring that projects move forward efficiently by integrating efforts across clinicians, researchers, data scientists, and institutional teams.

While exposure to data analysis and machine learning is part of the role, the primary focus is on research coordination, regulatory processes, and multidisciplinary collaboration. As a Research Assistant (RA), you will play a hands-on technical role with increasing intellectual ownership in running, querying, and analyzing large-scale clinical datasets using Snowflake, SQL, and Python, supporting the development, evaluation, and deployment of machine learning models for conference submissions and peer-reviewed manuscripts within one of the nation’s largest healthcare environments.

You will work closely with clinicians, data scientists, PhD students, and engineers to translate complex clinical questions into integrated results in SharePoint and Power

BI.

This position offers opportunities to contribute intellectually to study design, methodological development, and scientific writing, with mentorship tailored to candidates planning to pursue PhD or MD–PhD training in computer science, machine learning, biomedical informatics, or related fields. Research Assistants at TIRIC gain direct exposure to real-world clinical data at scale, rigorous applied machine learning research, and collaborative academic environments that support long-term research and academic career development.

This role is ideal for a self-driven, technically strong candidate seeking deep exposure to applied machine learning, healthcare data infrastructure, and translational research, without significant direct patient interaction. TIRIC is deeply committed to mentorship and career development, and this role is designed to prepare trainees for careers in academia, biomedical AI, public health, or biomedical research.

Responsibilities include, but are not limited to, the following:

• Querying and managing large-scale clinical datasets using Snowflake and SQL
• Running and maintaining data pipelines in Snowflake using Python and related tools (e.g., Pandas, Num Py)
• Performing data cleaning, validation, quality control, and documentation
• Organizing multimodal datasets (EMR, imaging-derived variables, clinical outcomes)

• Supporting the development, running, and evaluation of machine learning models
• Troubleshooting analytical workflows in collaboration with MIT data scientists
• Conducting exploratory data analysis and feature engineering

• Analyzing quantitative and qualitative data
• Generati…