A cover letter is required for consideration for this position and should be attached as the first page of your resume. The cover letter should address your specific interest in the position and outline skills and experience that directly relate to this position.

Upload your cover letter and resume to the linked Dropbox as a single file.
 

The School of Information seeks two Instructional Aides (IAs) for SI 671/721: Data Mining: Methods and Applications, a graduate-level course with mostly masters and some doctoral students. IAs will lead weekly lab/discussion sections, prepare instructional materials, manage the course Canvas site, and grade programming assignments, quizzes, and exams. IAs will report to the course instructor, Prof. Paramveer Dhillon, and coordinate with one Graduate Student Instructor (GSI) to deliver course support. The course covers topics including mining itemsets, matrix data, sequences and text, time series, networks, streaming data, embedded representations, and causal inference using Python-based tools.

Course Details
SI 671/721 is a graduate-level course on advanced topics in data mining. The course provides an overview of recent research topics in the field of data mining, state-of-the-art methods to analyze different types of datasets, and their applications to real-world problems. The course emphasizes practical applications of data mining rather than the theoretical foundations of machine learning and statistical computing, and is suitable for students conducting research in data mining as well as those who apply data mining techniques in allied disciplines such as natural language processing, network science, human-computer interaction, economics, and business intelligence.

More information about this course can be found on U-M's Course Catalog via Wolverine Access.

• Lead weekly lab/discussion sections covering applied data mining topics such as itemset mining, matrix decomposition, network analysis, and time-series modeling.
• Prepare and organize instructional materials, including lab exercises, code demonstrations, and supplementary resources.
• Manage and maintain the course Canvas site, including posting assignments, lecture materials, and announcements.
• Grade programming assignments, quizzes, and midterm and final exams, and provide timely written feedback to students.
• Hold office hours as needed to support students with course content, programming assignments, and the final project.
• Coordinate with the instructor and GSI on course logistics, grading rubrics, and assignment scheduling.
   

• Proficiency in Python and working familiarity with data science libraries such as NumPy, Pandas, Matplotlib, and Scikit-Learn.
• Completion of coursework in probability, statistics, and linear algebra, or equivalent demonstrated knowledge.
• Prior coursework or equivalent experience in data mining, machine learning, or statistical learning.
• Strong written and verbal communication skills, with the ability to explain technical concepts clearly to graduate students.
• Flexibility to work occasional hours outside the regular schedule to support grading deadlines and exam administration.
• Graduate students are not eligible to apply
   

• Prior experience as an instructional aide, grader, or tutor in a quantitative or computational course.
• Familiarity with Canvas or similar learning management systems.
• Exposure to topics covered in SI 671/721, such as network mining, time-series analysis, dimensionality reduction, or text mining.
   

Positions that are eligible for hybrid or mobile/remote work mode are at the discretion of the hiring department. Work agreements are reviewed annually at a minimum and are subject to change at any time, and for any reason, throughout the course of employment. Learn more about the work modes.

School of Information, Leinweber Building, University of Michigan, Ann Arbor - This is an in-person position with an occasional need or flexibility for remote work. Hybrid work is possible and follows the UMSI Remote Work Policy and with supervisor approval. Remote work agreements are reviewed annually and are subject to change depending on our needs.

The University of Michigan is an equal employment opportunity employer.