What are the responsibilities and job description for the ASSISTANT TEACHING PROFESSOR OF APPLIED AI AND DATA SCIENCE position at Carnegie Mellon University?
Description
The Heinz College of Information Systems and Public Policy at Carnegie Mellon University is seeking qualified candidates for the open position of Assistant Teaching Professor of Applied AI and Data Science. Our students are primarily at the masters level, with a diverse range of education and backgrounds.
We invite academics or professionals with a passion for teaching and experience in applying and instilling modern data science practices. These practices include the design, deployment, and operationalization of AI and data science solutions from inception to implementation.
The instructor will be responsible for developing and communicating culturally responsive and inclusive coursework and objectively assessing student performance. The instructor will design and oversee student projects and play an active role in meta curricular activities like advising students, supporting curricular advancement, clubs, and competitions.
The ideal candidate will have a strong foundation in applying data science practices to address business and societal problems.
Key Responsibilities
Teach Machine Learning:
We prepare students to understand and leverage technology responsibly to effect change in business and society. Our students are trained to collect and analyze data in pursuit of positive transformation. We teach a set of data governance and analytical skills with a focus on the effectiveness, equity, and integrity in the decision process and its ramifications. Armed with this unique set of skills, Heinz College graduates are in great demand across all sectors of the economy.
Qualifications
The Heinz College of Information Systems and Public Policy at Carnegie Mellon University is seeking qualified candidates for the open position of Assistant Teaching Professor of Applied AI and Data Science. Our students are primarily at the masters level, with a diverse range of education and backgrounds.
We invite academics or professionals with a passion for teaching and experience in applying and instilling modern data science practices. These practices include the design, deployment, and operationalization of AI and data science solutions from inception to implementation.
The instructor will be responsible for developing and communicating culturally responsive and inclusive coursework and objectively assessing student performance. The instructor will design and oversee student projects and play an active role in meta curricular activities like advising students, supporting curricular advancement, clubs, and competitions.
The ideal candidate will have a strong foundation in applying data science practices to address business and societal problems.
Key Responsibilities
Teach Machine Learning:
- Teach students to implement, optimize, and evaluate machine learning workflows using relevant Python libraries. Emphasize end-to-end practices, from data preprocessing and model training to deployment and monitoring in real-world systems.
- Instruct students in modern AI techniques for unstructured data analysis, including generative AI approaches such as retrieval-augmented generation (RAG), fine-tuning, and transfer learning.
- Introduce students to the use of open-source models for processing and analyzing unstructured data, including text, images, and multimedia.
- Teach students the strengths and limitations of traditional analytics techniques. Guide students in selecting the appropriate technique in addressing business, policy, and societal challenges.
- Apply feature engineering methods to different types of data to improve the performance of machine learning models.
- Guide students in choosing appropriate models for datasets by evaluating their performance and understanding the advantages and disadvantages of each.
- Analyze and describe the societal impacts of machine learning methods implemented in real-world datasets, considering ethics, bias, and fairness.
- Leverage data storytelling techniques to report insights on machine learning model outputs.
We prepare students to understand and leverage technology responsibly to effect change in business and society. Our students are trained to collect and analyze data in pursuit of positive transformation. We teach a set of data governance and analytical skills with a focus on the effectiveness, equity, and integrity in the decision process and its ramifications. Armed with this unique set of skills, Heinz College graduates are in great demand across all sectors of the economy.
Qualifications
- Experience in having applied data science techniques in real world settings
- Proficiency in using Python development environments
- Strong understanding of machine learning concepts and techniques
- Excellent communication and interpersonal skills
- Ability to develop and deliver engaging course materials
- Commitment to fostering an inclusive classroom environment that values diverse perspectives
- An advanced degree in a STEM field or equivalent professional or teaching experience
- Interest in applying advanced analytical practices for societal benefit
