Adjunct Instructor in Clinical Data Science and Machine Learning

Brandeis University•Waltham, MA

5d•$6,573•Remote

About The Position

Brandeis University’s Online Health Informatics Program is seeking an Adjunct Faculty member for RHIN 151 Clinical Data Science and Machine Learning for the Fall-1 2026 session. This 3-credit asynchronous online course is an 8-week requirement for the Master of Science in Health Informatics. This course will review methods for preprocessing clinical data, handling censoring and missingness, model development and validation in healthcare contexts, calibration, subgroup fairness, and clinical utility assessment. It will provide a comprehensive understanding of how clinical data science and machine learning impact healthcare data in addition to guidance on programming methods to process data.

Requirements

Advanced degree (Master’s or Ph.D.) in Data Science, Computer Science, Health Informatics, or a related field.
Minimum of 5 years professional experience in an analytics or leadership role with a focus on machine learning and using data science to improve healthcare outcomes
Strong knowledge of statistical analysis, programming languages (e.g. Python, R), and data management
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.
Basic programming skills (Python or R) and knowledge of statistics are strongly recommended prior to enrollment in the course.

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

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