Adjunct Lecturer, Political Analytics – Big Data & Political Strategy (On-Campus, Spring 2024)

New York City, United States

Columbia University

To advance, adapt and accelerate careers.

View company page

Company Description

Columbia University has been a leader in higher education in the nation and around the world for more than 250 years. At the core of our wide range of academic inquiry is the commitment to attract and engage the best minds in pursuit of greater human understanding, pioneering new discoveries, and service to society.

The School of Professional Studies at Columbia University offers innovative and rigorous programs that integrate knowledge across disciplinary boundaries, combine theory with practice, leverage the expertise of our students and faculty, and connect global constituencies. Through eighteen professional master's degrees, courses for advancement and graduate school preparation, certificate programs, summer courses, high school programs, and a program for learning English as a second language, the School of Professional Studies transforms knowledge and understanding in service of the greater good.

Job Description

The School of Professional Studies M.S. in Political Analytics program is seeking industry professionals with experience in political science and data analytics to serve as a part-time Lecturer for an on-campus graduate-level course in Big Data & Political Strategy.

This position will involve two components. First, the lecturer will be required to develop the course syllabus, source course materials, create teaching plan, and make iterative changes based on committee review and feedback. Second, the lecturer will teach the course in-person on-campus in a 14-week semester schedule.

In this course, students will learn about the range of big data sources that can be gathered and aggregated, including public data, traditional and social media data, consumer and transactional data, web data, and data from machines and sensors. Students will become familiar with the ways in which both structured and unstructured data can be used to gain insights about political actors’ sentiments, attitudes, and opinions and to develop strategies to predict and prompt behavior.


● Lead class lectures, instructional activities, and classroom discussion.
● Evaluate, grade student work and assessments.
● Monitor and address student concerns and inquiries.
● Conduct weekly office hours.


Columbia University SPS operates under a scholar-practitioner faculty model, which enables students to learn from faculty that have outstanding academic training as well as a record of accomplishment as practitioners in an applied industry setting. 


  • Graduate degree in an area related to political science, data science, quantitative methods or related field.
  • 5+ years of professional experience in a role involving political analytics.
  • Knowledge of statistics, econometrics, quantitative analysis or related techniques.
  • Knowledge and familiarity with analysis software (e.g. R).

Preferred Skills & Experience

  • Doctoral degree or equivalent in a relevant field.
  • Prior experience electoral campaigns, policy-making initiatives, advocacy efforts, or lobbying operations.
  • University teaching experience.

Additional Information

Salary: 12,985.42 per semester-length course development and teaching

  • Please submit a resume inclusive of university teaching experience.
  • Must reside and be eligible to work in the United States.

Your information will be kept confidential according to EEO guidelines.
Columbia University is an Equal Opportunity/Affirmative Action employer

Apply now Apply later
  • Share this job via
  • or

* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰

Tags: Big Data Data Analytics Econometrics R Statistics Teaching Unstructured data

Perks/benefits: Career development

Region: North America
Country: United States
Job stats:  6  0  0

More jobs like this

Explore more AI, ML, Data Science career opportunities

Find even more open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general - ordered by popularity of job title or skills, toolset and products used - below.