Computational Research Associate

Mass General Brigham•Boston, MA

1d•$53,040 - $75,889•Hybrid

About The Position

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. Summary: Collaborates with team members to assist in collecting, analyzing, and interpreting data sets. Essential Functions-Writes and runs basic queries as needed. -Assists in maintaining existing reporting systems. -Contributes to presentations to key stakeholders. -Works with senior analysts to design, maintain, and distribute reports. -Learn leading practice analysis methodologies. -Conduct continuous analysis of current performance and accurately forecast data trends. Position Overview Dr. Kenneth Westerman is recruiting a Computational Research Associate to join his interdisciplinary research group at Massachusetts General Hospital (MGH) and Harvard Medical School (HMS). The associate will contribute to cutting-edge genetic epidemiology analyses in large biobank and cohort datasets, with implications for precision medicine and cardiometabolic disease prevention. The position will be co-supervised by Dr. Arun Durvasula at the University of Southern California (USC). This is an excellent opportunity for a motivated early-career researcher to develop advanced computational skills, build a strong publication record, and be a part of a world-class research community at MGH, HMS, and the Broad Institute. Key Responsibilities The associate will work with Dr. Westerman to investigate genetic factors modifying the relationship between non-genetic exposures (such as adiposity and age) and cardiometabolic risk factors. Specific responsibilities include: Conducting gene-environment interaction analyses in large-scale biobank and cohort datasets Harmonizing heterogeneous datasets including socioeconomic and lifestyle covariates and cardiometabolic trait phenotypes Contributing to the preparation of scientific manuscripts and presenting findings at internal group meetings and external conferences Collaborating with an interdisciplinary team spanning computational scientists and clinician-researchers across USC, MGH, HMS, and the Broad Institute Beyond this primary focus, there are opportunities to become involved with projects from our broader portfolio including polygenic risk scores, multi-omics analysis, and classical epidemiology in large cohort datasets.

Requirements

Bachelor’s or master’s degree in genetic epidemiology, computational biology, bioinformatics, statistics, or a related field
Strong programming skills in R and/or Python and comfort with Linux/shell scripting
Excellent written and verbal communication skills
Highly self-motivated and driven to contribute to scientific research

Nice To Haves

Experience with biomedical cloud computing environments (e.g., Terra, DNAnexus) and large-scale genomic datasets (e.g., UK Biobank)
Experience with genome-wide association studies and gene-environment interaction analysis
Background and interest in cardiometabolic disease biology

Responsibilities

Writes and runs basic queries as needed.
Assists in maintaining existing reporting systems.
Contributes to presentations to key stakeholders.
Works with senior analysts to design, maintain, and distribute reports.
Learn leading practice analysis methodologies.
Conduct continuous analysis of current performance and accurately forecast data trends.
Conducting gene-environment interaction analyses in large-scale biobank and cohort datasets
Harmonizing heterogeneous datasets including socioeconomic and lifestyle covariates and cardiometabolic trait phenotypes
Contributing to the preparation of scientific manuscripts and presenting findings at internal group meetings and external conferences
Collaborating with an interdisciplinary team spanning computational scientists and clinician-researchers across USC, MGH, HMS, and the Broad Institute