Predoctoral Scholar - Machine Learning Data Scientist - JR27067-3800

University of Chicago - Chicago, IL

Full-time - Entry Level

Chicago, IL

Educational Services

About the position

The Harris School of Public Policy at The University of Chicago is seeking a dedicated and skilled individual for a one-year Research Professional position, specifically as a Predoctoral Scholar in Machine Learning Data Science. This role is designed for candidates with a strong interest in education research and aims to contribute to the development of innovative policies through rigorous data analysis and machine learning techniques. The successful candidate will work under the supervision of Professor Anjali Adukia, engaging in a collaborative environment that emphasizes the importance of evidence-based policymaking. In this position, the scholar will be responsible for collecting, transforming, and processing raw and unstructured data into a usable format suitable for data science applications. The role involves developing classification models using both supervised and unsupervised machine learning techniques, as well as deep learning methods tailored for various research applications. Additionally, the scholar will design and implement natural language processing (NLP) algorithms, focusing on text preprocessing, feature extraction, sentiment analysis, semantic role labeling, and document classification. The position also requires the visualization of complex data for inclusion in academic manuscripts and presentations, necessitating strong documentation skills and the use of version control systems like GitHub. Collaboration with a team of researchers is essential, as the scholar will assist in analyzing data to extract relevant information and support various programs and initiatives. The role may also involve ad-hoc assistance to faculty or staff members with data manipulation, statistical applications, programming, analysis, and modeling tasks. The scholar will be expected to perform other related work as needed, contributing to the overall mission of the Harris School to drive social change through effective policymaking.

Responsibilities

• Collect, transform, and process raw and unstructured data into a usable data science format.
• Develop classification models using supervised and unsupervised machine learning/deep learning techniques for various research applications.
• Design and implement NLP algorithms and techniques for text preprocessing, feature extraction, sentiment analysis, semantic role labeling, and document classification.
• Visualize complex data for inclusion in manuscripts and presentations.
• Write documentation and use version control on GitHub, including communicating and documenting your process to other researchers on the project.
• Work collaboratively with a team of researchers.
• Assist in analyzing data for the purpose of extracting applicable information.
• Perform research projects that provide analysis for a number of programs and initiatives.
• Assist staff or faculty members with data manipulation, statistical applications, programming, analysis and modeling on a scheduled or ad-hoc basis.
• Perform other related work as needed.

Requirements

• Minimum requirements include a college or university degree in a related field.
• Proficiency in Stata; ability to use other statistical software packages (e.g., R, Python, or BASH).
• Significant coursework or experience in data analysis.
• Exceptional technical skills and a proven ability to creatively tackle difficult empirical problems.

Nice-to-haves

• Bachelor's Degree in Economics, Statistics or related disciplines.
• Previous research experience.
• Experience with NLP techniques such as word and document embeddings.
• Experience using clustering and dimensionality reduction techniques such as K-means and PCA.
• Experience implementing Neural Networks using Tensorflow, PyTorch, Scikit-Learn, OpenCV, deep learning, and other artificial intelligence techniques.
• Experience with image classification/recognition and tools to analyze illustrations in addition to photographs.
• Familiarity with Linux, UNIX, and High Performance Computing environment.
• Experience forming and testing hypotheses.
• Drive to learn new programming or data analysis techniques.

Benefits

• Collaborative work environment with a focus on innovation and social change.
• Opportunity to work under the supervision of experienced faculty members.
• Access to advanced data science tools and methodologies.