Research Assistant

Research Assistant Position at Bhupathiraju Lab

The Bhupathiraju Lab at Channing Division of Network Medicine (Department of Medicine, Mass General Brigham) is seeking a part-time Research Assistant to support an NIH-funded project investigating metabolomic signatures of flavonoid rich foods and their relationships with type 2 diabetes. The Research Assistant will work under the supervision of Dr. Bhupathiraju and will focus on high-dimensional data analysis of metabolomics and dietary datasets using R and Python.

This position is well suited for a quantitatively oriented candidate interested in metabolomics, nutritional epidemiology, and data science.

• Import, clean, and manage large, high-dimensional datasets, including metabolomic profiles, dietary intake, and clinical covariates.
• Conduct statistical and multivariable analyses in R and/or Python, including:
• Data preprocessing and normalization of metabolomics data
• Dimension reduction (e.g., PCA) and clustering methods
• Regression and other modeling approaches to relate diet to metabolomic patterns and cardiometabolic outcomes.
• Create reproducible analysis pipelines and documentation (R scripts, Python notebooks, Git/Git Hub version control).
• Generate tables, figures, and visualizations for abstracts, manuscripts, and presentations.
• Assist with interpretation of findings and drafting of analytic and methods sections.
• Participate in regular lab and project meetings; present interim analyses as needed.
• Adhere to all Mass General Brigham policies regarding data security, privacy, and research compliance (including IRB requirements and HIPAA).
• Perform other related duties as assigned.

Required:

• Bachelor's degree in Biostatistics, Epidemiology, Data Science, Computer Science, Bioinformatics, Nutrition, or a related quantitative field; or current enrollment in a MPH program.
• Demonstrated experience working with high-dimensional or large epidemiologic data.
• Proficiency in R and/or Python for data management, statistical analysis, and visualization.
• Working knowledge of machine learning methods
• Strong organizational skills, attention to detail, and ability to work both independently and as part of a team.
• Excellent written and oral communication skills.

Preferred:

• Prior experience with metabolomics or other omics data (e.g., genomics, proteomics).
• Experience with R packages such as tidyverse, data.table, lme4, survival, glmnet, or Python libraries such as pandas, numpy, scikit-learn, and stats models.
• Familiarity with dietary assessment data (e.g., FFQs, 24-hour recalls) and/or nutritional epidemiology.
• Experience with reproducible research tools (R Markdown, Quarto, Jupyter, Git/Git Hub).
• Experience contributing to scientific manuscripts or conference abstracts.

• Part-time position (~10–20 hours/week); schedule can be arranged within standard weekday hours by mutual agreement with the PI.
• Work may be hybrid.

Remote Type:
Hybrid

Work Location:

41-45 Avenue Louis Pasteur

Scheduled Weekly

Hours:

20

Employee Type:
Regular

Work Shift:

Day (United States of America)

Pay Range: $21.00 - $29.01/Hourly

Grade: 5