Adjunct Instructor in Statistical Modeling and Causal Inference

Brandeis University•Waltham, MA

9d•$6,573•Remote

About The Position

Brandeis University’s Online Applied Data Science and Decision Analytics Program is seeking an Adjunct Faculty member for RADS 105 Statistical Modeling and Causal Inference for the Fall 1 2026 session. This 3-credit asynchronous online course is an 8-week requirement for the Master of Science in Applied Data Science and Decision Analytics. This course will cover regression, identification strategies, and causal inference techniques for evaluation of programs, products, and policies.

Requirements

Advanced degree (MS or Ph.D.) in Statistics, Biostatistics, Applied Mathematics, Data Science or a related field.
Experience in statistical modeling and causal inference, including regression analysis, identification strategies, and methods for addressing bias and confounding in observational data.
Ability to apply models to real-world program, product, or policy evaluation contexts.
Professional experience building regression, generalized linear models, hierarchical/multilevel models, and Bayesian approaches.
The ability to apply models to real‑world datasets and to incorporate statistical software such as SPSS.
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.

Nice To Haves

Prior online teaching experience at the graduate level.
Knowledge of global learner personas and culturally responsive pedagogy.
Familiarity with Moodle LMS and digital authoring tools (e.g., H5P).

Responsibilities

Organized and timely rollout of course content.
Maintaining consistent communication through weekly announcements.
Ensuring all instructional activities occur within university-approved digital platforms.
Building an active teaching persona by hosting live introductory sessions.
Facilitating weekly academic discourse in forums.
Maintaining regular availability for student consultation.
Providing transparent, rubric-based evaluations and supportive commentary on student work within a standardized weekly timeframe.
Adherence to university communication protocols.
Promotion of respectful online "netiquette".
Ensuring the course meets accessibility and technical visibility standards before and during the term.