Associate Lecturer - Big Data, Machine Learning and Society

Big Data and Machine Learning play a role in enabling policymakers to address important social and economic problems. Therefore, it is essential that data users and consumers can identify high-quality data and understand the implications of poor data analysis.

In this course, students will explore how big data and machine learning are being used in economics and social science research. This course provides a practical, skills-based approach. Students will be guided through the process and pitfalls of using and interpreting results based on big data from project inception to final data analysis. Practical skills will be developed through the completion of weekly data analysis tasks and coding exercises.

Topics covered include: poverty and disadvantage, education, gender disparities, Covid-19 impacts, and social-policy evaluations. In the context of these topics, the course will also provide an introduction to basic methods in data science, including machine learning for prediction, causal inference (econometric approaches and machine learning embellishments), and heterogeneous treatment effects.

This course will discuss the benefits and drawbacks of each of Machine Learning methods in a non-technical manner and through real-world case studies and applications.

Job Requirement

• Relevant bachelor’s or master’s degree with 5 years of work experience
• Candidate should possess a good understanding of econometrics
• Candidates without an Economics Degree should have a good machine learning and big data to core economics and econometric concepts working background.
• Preferably candidate should have some machine learning knowledge and Python experience
• At least 2 years of relevant teaching experience at the tertiary level is preferred
• Must be available to conduct a weekly 3-hour lecture for a period of 9 weeks
• Must be available to teach classes during the day between 8.30 AM and 6.30 PM

Additional Job Description

SIM-RMIT is looking for associates who are dedicated, highly motivated, and possess a strong passion for teaching and shaping the minds of the young generation. Candidates must be able to handle a sizeable class, manage assessment marking, and provide constructive feedback to students within a given timeline. Associates will work closely under the guidance of the course coordinator based in the partner university.