Dson Connected: Spring 2027 Applied Data Fellows (formerly Q-Fellows) Program
NOTE: The successful candidate will register for Dickinson’s Internship Program (INP). Please contact the internship team if you have any INP-related questions.
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The Applied Data Fellows Program (previously Q-Fellows), generously funded through the Quantitative Experiential Learning Fund, provides an exciting opportunity for students to engage in hands-on, data-driven projects across a variety of campus partners (staff, faculty, committees, departments, centers, etc.). As an Applied Data Fellow, you will collaborate with faculty and/or staff to support quantitative and data-related research, analysis, and problem-solving. This program is designed to help students enhance their technical and analytical skills while contributing to impactful projects across campus.
Responsibilities:
- Work on data and quantitative-focused projects in collaboration with faculty and/or staff from diverse academic disciplines.
- Perform tasks such as data cleaning, analysis, visualization, statistical modeling, and troubleshooting technical issues.
- Engage in regular communication with project supervisors to clarify goals, timelines, and deliverables.
- Participate in regularly scheduled (weekly or bi-weekly) meetings with the Program Director of Applied Data Intelligence and/or other assigned faculty/staff mentor, who will provide guidance, feedback, and project oversight.
- Manage project deadlines while maintaining a professional and proactive approach.
- Document processes and outcomes for each project to support transparency and reproducibility.
- Present the outcomes of your work in a final meeting with the campus partner.
Qualifications:
- Current undergraduate student with a strong interest in quantitative research and data analysis.
- Preferred GPA of 3.0 or higher
- Juniors or seniors preferred, sophomores welcome to apply
- Proficiency in at least one data analysis tool or programming language (e.g., Excel, R, Python, SPSS, Stata, Tableau). Experience with Power BI may also be beneficial.
- Strong problem-solving skills, analytical thinking, and attention to detail.
- Effective written and verbal communication skills, with the ability to present complex information clearly.
- Ability to work independently and manage multiple priorities effectively.
- Willingness to learn new tools and methods for quantitative research.
- Prior experience with quantitative research or data analysis is preferred but not required.
Time Commitment:
One academic semester with set weekly hours in the QR Center (approximately 10–12 hours per week, depending on the project and schedule). The 18-hour on-campus employment rule will apply. Fellows hired in the Fall will be given the option to continue working in the Spring semester.
Supervision:
Q-Fellows will report to the Program Director of Applied Data Intelligence, who will oversee project assignments, provide regular feedback, and support your professional development. You will meet regularly with your project’s Campus Partner.
Funding and Support:
This position is made possible through the Quantitative Experiential Learning Fund, which supports student engagement in meaningful, data-driven work across disciplines. Fellows will receive an hourly wage.
Benefits:
- Gain hands-on experience in quantitative and data-related projects.
- Build a professional portfolio of work that spans multiple academic fields.
- Enhance your technical, analytical, and communication skills.
- Develop professional connections with faculty, staff, and peers across campus.
- Strengthen your preparation for future academic or career opportunities in data-related fields.
How to Apply:
Interested applicants should submit the following materials through Handshake by 11:59 p.m. on Nov. 15, 2026:
- A resume highlighting your quantitative and technical skills, specifically highlighting your proficiency in programming languages and data analysis tools.
- A brief cover letter explaining your interest in the Q-Fellows program, your relevant experience and whether you would want/need this experience to fulfill a requirement for your major.
- Unofficial transcript
- A sample of prior work involving data or quantitative analysis (optional, but encouraged)