Adjunct Associate Faculty, Applied Machine Learning I (On-Campus, Fall '26)
This adjunct role focuses on teaching graduate students foundational supervised learning techniques and hands-on data analysis workflows, emphasizing practical applications in business, healthcare, and beyond. The instructor will guide students through data exploration, predictive modeling, and insight generation using real-world datasets.
Skills required
About this role
The School of Professional Studies seeks a data analytics professional to serve as a part-time Associate for a graduate-level course called Applied Analytics Frameworks & Methods I.
- The world is generating data at an even faster pace via business transactions, online searches, social media activities, and various sensors. The ready availability of vast amounts of data creates opportunities to predict outcomes and explain phenomena across a wide range of domains from medicine to business to even space exploration. Supervised learning techniques are being extensively used to make useful predictions and generate insights to tackle problems. These predictive analysis techniques focus on this course, guiding students through the data-wrangling process, starting with data exploration and other foundational approaches. The course then covers an array of supervised learning techniques, including linear regression, decision trees, and support vector machines. Students also have the opportunity to challenge themselves in applying and combining the techniques they have learned through a predictive analytics competition.
An Associate is a faculty line junior to a Lecturer that provides subject matter expertise and supports the instructional process for a course section. Serving as an Associate is an outstanding way to gain exposure to graduate-level teaching at Columbia University.
Responsibilities
- Attend all class sessions, assist with instruction, lead breakout sessions, facilitate discussions.
- Evaluate, grade student work and assessments as requested by the course Lecturer.
- Monitor and address student concerns and inquiries.
Qualifications
Columbia University SPS operates under a scholar-practitioner faculty model, which enables students to learn from faculty possessing outstanding academic training as well as a record of accomplishment as practitioners in an applied industry setting.
Requirements
- Graduate degree in an area related to data science, applied analytics, statistics, or another quantitative discipline.
- Proficient in Python programming.
- 3+ years of professional experience in a role involving applied analytics.
Preferred Skills & Experience
- Knowledge of theories and practical application of machine learning.
- University teaching experience.
Additional Information
Salary range: $2,000 - $3,000 per semester long course
Please submit a resume inclusive of university teaching experience.
All your information will be kept confidential according to EEO guidelines.
Columbia University is an Equal Opportunity Employer / Disability / Veteran
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