Remote Computer Science Student Jobs in Odenton, MD

Position Details
Position Information
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If this position title includes the label On-Campus FWS or Off-Campus FWS, it is for students with a Federal Work Study (FWS) award for the current school year ONLY. Applicants to these positions will be required to verify this by attaching their FWS Program Student Statement as a required document. Do not apply unless you meet this criterion.
Position Title
Student Research Specialist III _Non-FWS
Level of Support
Student Support III
Position Type
Non-FWS
Number of Openings
1
Work Hours
During Standard Business Hours (M-F 8am-5pm)
Wage Per Hour
20
Division/School
School of Medicine and Health Sciences
Department Name
811411 PSYCHIATRY-SCHOOL-INSTRUCTION
Department Bio
Founded in 1824, the GW School of Medicine and Health Sciences (SMHS) is the 11th oldest medical school in the country and the 1st in the nation's capital. Since its establishment, SMHS has been at the forefront of medical education and has grown to include highly-ranked programs in the health and biomedical sciences. With dozens of top-tier residencies, the school continues to be a competitive and esteemed destination for medical school graduates, faculty, and researchers across the country. Find out more here: http://smhs.gwu.edu/.
Position Specific Summary
The Center for Global Mental Health Equity is seeking a student Research Assistant beginning in Fall 2026. The position requires approximately 20 hours per week and will support data management, statistical analysis, visualization, and applied AI/ML workflows across multiple mental health research projects in domestic and global settings. The ideal candidate will have experience or training in data science, statistics, machine learning, or a related quantitative field, and an interest in applying computational methods to health research. The candidate should be proficient in Python and comfortable working in notebook environments (e.g., Jupyter, Google Colab) with version-controlled, reproducible workflows. Working closely with faculty, staff, and interdisciplinary research teams, the student will contribute to data-driven and AI-enabled research addressing barriers to accessible and effective mental health care.
Duties

• Assist with data management, cleaning, processing, and quality assurance for research studies, including multi-country program datasets.
• Develop data visualizations, dashboards, and figures to communicate research findings to academic, clinical, and program stakeholders.
• Conduct statistical analyses and support interpretation of quantitative findings, including support for meta-analysis and systematic review workflows.
• Support the development, testing, and evaluation of AI/ML and natural language processing pipelines used in mental health research applications (e.g., text classification, transcript coding, model output evaluation).
• Support reproducible analysis workflows and documentation using tools such as Git/GitHub and Python notebooks.
• Work with large and complex datasets from a variety of research projects, ensuring data privacy and ethical handling of sensitive health data.
• Contribute to literature reviews and synthesis of quantitative research findings.
• Collaborate with faculty, staff, and interdisciplinary research teams on ongoing projects.
• Assist with preparation of manuscripts, reports, presentations, and grant-related materials.
• Perform other research-related duties as assigned.

Required Qualifications

• Proficiency in Python for data cleaning, transformation, statistical analysis, and visualization, including core libraries (e.g., pandas, NumPy, matplotlib/plotly).
• Coursework or hands-on experience in data science, statistics, biostatistics, computer science, machine learning, public health, epidemiology, or a related quantitative field.
• Experience applying machine learning methods to real datasets (e.g., scikit-learn for classification, regression, or model evaluation), through coursework, personal projects, or prior research.
• Familiarity with version control (Git/GitHub) and reproducible, well-documented analysis workflows, or willingness to learn.
• Ability to work with large, complex, and messy datasets and to independently perform data quality assurance.
• Experience creating data visualizations, figures, or dashboards to communicate findings to non-technical audiences.
• Understanding of research data ethics and the responsible handling of sensitive or identifiable health data.
• Strong attention to detail, data quality, and ability to manage multiple tasks and meet deadlines.
• Ability to work both independently and collaboratively in an interdisciplinary team.

Additional Preferred Qualifications

• Familiarity with deep learning or natural language processing tooling (e.g., PyTorch/TensorFlow, Hugging Face transformers, or LLM APIs).
• Experience with multi-country or large-scale survey data (e.g., MICS, DHS).
• Experience working with clinical/health datasets or in global health research.
• Familiarity with cloud environments or data pipeline tooling.
• Open-source contributions or a public portfolio of data science or machine learning work.

Standard Position Description
The student will perform administrative and research tasks under general supervision by a faculty member or research coordinator. Tasks may include: contribute to peer reviewed research and write research memos of findings, assist with presentations, supervision and training of other student staff, write and run statistical programs to analysis data. Special projects or other duties may be assigned related to specific departmental/faculty needs.
Professional Outcomes
Critical Thinking, Communication, Teamwork, Leadership, Professionalism, Technology and Data, Career Development
Required Qualifications
Previous skills, training, or experience is required.
Preferred Qualifications
Posting Detail Information
Job Open Date
08/04/2026
Job Close Date
08/31/2026
Campus Location
Foggy Bottom
Work Type Designation
Hybrid (mixture of in-person and virtual work)
Hiring Manager Name
Sauharda Rai
Special Instructions Summary
If applicable, please submit a link to your GitHub profile (or a portfolio of relevant work), highlighting repositories or contributions demonstrating experience with Python, data analysis, visualization, or machine learning/deep learning. Personal projects, coursework, and open-source contributions are welcome.
EEO Statement
The university is an Equal Employment Opportunity/Affirmative Action (EEO/AA) employer committed to maintaining a nondiscriminatory, harassment-free, diverse work and education environment. The university does not unlawfully discriminate on the basis of protected characteristics or on any other basis prohibited by applicable law in any of its programs, activities, or employment practices.
For more information on this policy and its purpose, please read the Equal Employment Opportunity Policy Statement.
Background Check
The student employee will:
In this position, the student will not perform any functions and/or have access to any financially, safety, or security sensitive items listed above. - Background check not required.