Adjunct Instructor in Talent Analytics and Workforce Metrics

Brandeis University’s Online Industrial Organizational Psychology Program is seeking an Adjunct Faculty member for RIOP 110 Talent Analytics and Workforce Metrics for the Fall 1 2026 session. This 3-credit asynchronous online course is an 8-week requirement for the Master of Science in Industrial Organizational Psychology.

This course will use applied methods for workforce analytics covering KPI design, descriptive and predictive modeling (turnover, hiring, performance), and dashboard development for HR stakeholders. Emphasis on translating data into actionable workforce strategy.

Core Course Responsibilities Summary

• Course Logistics and Facilitation: Focuses on the organized and timely rollout of course content, maintaining consistent communication through weekly announcements, and ensuring all instructional activities occur within university-approved digital platforms.

• Instructor Presence and Engagement: Centers on building an active teaching persona by hosting live introductory sessions, facilitating weekly academic discourse in forums, and maintaining regular availability for student consultation.

• Individual Feedback and Grading: Emphasizes the professional obligation to provide transparent, rubric-based evaluations and supportive commentary on student work within a standardized weekly timeframe.

• Professional Conduct and Standards: Requires adherence to university communication protocols, the promotion of respectful online "netiquette," and ensuring the course meets accessibility and technical visibility standards before and during the term.

Qualifications:

• Required:

• Advanced degree (Master’s, Ph.D. or PsyD) in Industrial-Organizational Psychology, Psychometrics, Statistics, Applied Psychology or Human Resource Management (with a quantitative focus), Behavioral Science, Data Science or a related field.

• Minimum 2 years professional experience within diverse industries or sectors, with a focus on HR/people/workforce analytics, psychometrics, HR business partnering or strategy development (with quantitative focus), talent management (with quantitative focus), organizational effectiveness, personnel or workplace research, applied data science or related roles.

• Strong knowledge of methods for developing descriptive dashboards and predictive models for retention and hiring decisions, defining workforce KPIs and aligning metrics to organizational objectives, and critically assessing data quality, bias risks, and model limitations in HR contexts.

• At least 1 year of teaching or training experience (preferably online/asynchronous)

• Experience with online instruction

• Excellent communication and teaching skills in an online learning environment.

• Preferred:

• Prior online teaching experience at the graduate level

• Familiarity with online education platforms and course development tools.

Interested candidates should submit:

A cover letter highlighting relevant qualifications and teaching experience.

A current CV or resume.

Contact information for three professional references.

Application review begins 5/28/2026 though we will continue to accept submissions on an ongoing basis.

This appointment is to a position that is in a collective bargaining unit represented by SEIU Local 509.

Compensation for this position is $6573.15

Pay Range Disclosure

The University's pay ranges represent a good faith estimate of what Brandeis reasonably expects to pay for a position at the time of posting. The pay offered to a selected candidate during hiring will be based on factors such as (but not limited to) the scope and responsibilities of the position, the candidate's work experience and education/training, internal peer equity, and applicable legal requirements.

Equal Opportunity Statement

Brandeis University is an equal opportunity employer which does not discriminate against any applicant or employee on the basis of race, color, ancestry, religious creed, gender identity and expression, national or ethnic origin, sex, sexual orientation, pregnancy, age, genetic information, disability, caste, military or veteran status or any other category protected by law (also known as membership in a "protected class").