Position Title:Senior Director of Development and Athletics Initiatives Position Type:Regular Hiring Range: $129,000.00 to $161,265.00; Compensation will be based on education, experience, skills relevant to the role, and internal equity. Pay Frequency:Annual OVERVIEW University Relations builds Santa
Department: UL Classification: Professional Faculty Job Category: Administrative or Professional Faculty Job Type: Full-Time Work Schedule: Full-time (1.0 FTE, 40 hrs/wk) Location: Fairfax, VA Workplace Type: Hybrid Eligible Sponsorship Eligibility: Not eligible for visa sponsorship Salary:
Type of Appointment: Full-Time, At-Will Job Classification: Administrator II Anticipated Hiring Range: $100,000-$115,008 annually (Commensurate with skills and qualifications) Work Hours: Monday - Friday, 8am - 5pm, unless otherwise notified Recruitment Closing Date: September 7, 2026
POSITION CODE: 7821 DEPARTMENT/ADMINISTRATION: Office of Enrollment POSITION: Administrative, Exempt (12 months), Full-time (40 Hours) Hybrid Schedule: Two days remote and three days onsite per week SALARY RANGE: $70,304-$70,304 HIRING RANGE: Anticipated hiring range is near or
Applications must be submitted no later than 11:59 p.m. CT on the day before the listed closing date. Weekly Work Hours19.5 Compensation RangeH07 Hourly Rate$23.78 Hourly FLSAUnited States of America (Non-Exempt) Work Location All positions are
Associate Director, Research Services OR Director, Research Services - (26004669) Description Information Technology Services intends to hire either an IT Manager (PIM1) or an IT Director (PIM2), based on the qualifications of the successful candidate. Information
Applications must be submitted no later than 11:59 p.m. CT on the day before the listed closing date. Weekly Work Hours19.5 Compensation RangeH07 Hourly Rate$23.78 Hourly FLSAUnited States of America (Non-Exempt) Work Location All positions are
Essential Duties and Responsibilities: - Perform hands-on data analysis and modeling with huge data sets. - Apply data mining, NLP, and machine learning (both supervised and unsupervised) to improve relevance and personalization algorithms. - Work side-by-side