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Data Scientist, Student Success Analytics

Job in Portsmouth, Virginia, 23701, USA
Listing for: James Madison University
Full Time position
Listed on 2026-11-01
Job specializations:
  • IT/Tech
    Data Analyst, Data Scientist
Salary/Wage Range or Industry Benchmark: 90000 - 130000 USD Yearly USD 90000.00 130000.00 YEAR
Job Description & How to Apply Below

Working Title:
Data Scientist, Student Success Analytics

State Role

Title:

N/A

Position Type:Administrative & Professional (Non-teaching Faculty)

Position Status: Full-Time

FLSA Status:Exempt:
Not Eligible for Overtime

College/Division: Student Academic Success

Department: 100756 - Student Success Analytics

Pay Band: N/A

Pay Rate: Commensurate with Experience

Specify Range or Amount:

Is this a JMU only position?No

Is this a grant-funded position?No

Is this a Conflict of Interest designated position?No

Beginning Review Date:11/21/2026

About JMU:

Mission

We are a community committed to preparing students to be educated and enlightened citizens who lead productive and meaningful lives.

Vision

To be the national model for the engaged university: engaged with ideas and the world.

Who We Are

Situated in the heart of Virginia’s beautiful Shenandoah Valley, the city of Harrisonburg is a vibrant community with a population of approximately 52,000. Harrisonburg is conveniently located approximately 120 miles from Washington, D.C. and Richmond, VA.

JMU is a selective, public institution with a growing national reputation for offering experiences that lead to an outstanding education and supportive environment for students, faculty and staff. The student body includes approximately 21,000 undergraduate and 1,800 graduate students, with over 1,000 full-time instructional faculty.

JMU offers thriving programs in the liberal arts, science and technology, and professional disciplines at the undergraduate, master’s and doctoral levels. JMU has achieved national recognition for the high quality of its academic programs, focus on maintaining strong student/faculty interaction, and innovative faculty research.

General Information:

Student Success Analytics (SSA) at James Madison University is accepting applications for a Data Scientist as part of its work toward leveraging data and technology for improving student success and retention. The Data Scientist is an applied analytics and research position focused on transforming student-success data into ethical, actionable evidence for institutional decisions. This position reports to the Director, works on a team within another data scientist and data engineer, and supports the strategic vision to achieve the highest level of data maturity that maximizes success outcomes for all students.

The Data Scientist will help shape the design, collection, and use of data and data analytics to ensure student success and retention. JMU is in the middle of an enterprise-wide system modernization program, and this position has unique opportunities to shape the paths forward for data ethics, data analytics, and the use of data for student success and data-informed decision-making. This position will help lead efforts as the university moves forward in establishing a student success culture informed by data from the Customer Relationship Management (CRM), Learning Management Software (LMS), Student Information System (SIS), and other systems.

The Data Scientist will work closely with Student Affairs, Planning, Analytics, & Institutional Research (PAIR), Information Technology, The Graduate School, the Libraries, and other departments. The person in this position would be joining at an exciting time as efforts are underway to build a knowledge community that develops and enhances expertise around how JMU will work toward a data maturity model that integrates data science into leadership decisions.

This position is telework eligible.

Duties and Responsibilities
  • Analyze data to generate actionable insights that can empower the early student success team and other stakeholders to act on data
  • Design and evaluate descriptive, predictive, and, where appropriate, causal or quasi-experimental analyses;…
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