Computational Associate

Mass General Brigham•Boston, MA

3d•Onsite

About The Position

The Research Data Analyst will work under the direction of the Principal Investigator to assist graduate students, post-doctoral fellows, clinical fellows, and others with research projects that focus on analytical and computational needs. The Computational Research Associate will join an interdisciplinary research team focused on advancing precision oncology through digital pathology, artificial intelligence, machine learning, genomics, and multi-omics research in genitourinary cancers. This is an exciting opportunity to contribute to cutting-edge translational research at the intersection of computational pathology, cancer biology, human genetics, and biomedical data science while collaborating with pathologists, clinicians, and researchers at Massachusetts General Hospital and Harvard Medical School.

Requirements

Experience as attained through education 0-1 year required
Understanding of human pathophysiology, hematologic function, pregnancy physiology and related fields of study.
Understanding of mathematical modeling, including dynamical systems, statistical analysis, and computational methods.
Ability to work collaboratively as part of a team and with supervision from team members.
Ability to work productively with scientists and clinicians at all levels.
Works in an organized manner with the ability to follow instructions, processes and timelines.
Can identify roadblocks occurring within areas of responsibility and refer them to the appropriate party(s) for assistance.
Strong computer skills, including accurate data entry.

Nice To Haves

Bachelor's or Master's degree in Computer Science, Bioinformatics, Computational Biology, Biomedical Engineering, Statistics, Data Science, Genetics, Epidemiology, or a related quantitative field
Proficiency in Python and/or R programming
Experience with or strong interest in machine learning, artificial intelligence, bioinformatics, computational biology, or biomedical data science
Strong analytical, organizational, communication, and collaboration skills
Experience with digital pathology, whole-slide image analysis, computer vision, or medical imaging applications
Familiarity with deep learning frameworks such as PyTorch or TensorFlow
Experience analyzing genomics, RNA sequencing, spatial transcriptomics, single-cell, or epigenomic datasets
Experience integrating and interpreting multiple biological data modalities across complex research projects

Responsibilities

Provide direct assistance with experimental studies.
Performs basic computational modeling and statistical analysis; prepares drafts of work for review; applies feedback received to create final analyses and/andor reports.
Meets with the PI and team to discuss work plans, activities, and results to support the applicability of efforts, learning opportunities, and professional development.
Reviews, collates, and prepares standard analyses of medical record data.
Perform administrative tasks, such as required for data collection; prepare documentation and record-keeping; update databases; run reports.
Conducts literature searches and submits appropriate articles to PI and team for consideration; identifies topics of interest for personal reading and may present at lab meetings.
Develop and apply machine learning and computer vision models for digital pathology and whole-slide image analysis.
Analyze genomic, transcriptomic, epigenomic, and spatial biology datasets.
Build computational workflows that integrate pathology images, molecular data, and clinical outcomes.
Support biomarker discovery and prognostic model development through multimodal data analysis.
Collaborate with a multidisciplinary team and contribute to scientific presentations, manuscripts, and publications.