Department
About the Department
Job Summary
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 using version control on GitHub, including communicating and documenting your process to other researchers on the project.
Work collaboratively with a team of researchers.
Assists in analyzing data for the purpose of extracting applicable information. Performs research projects that provide analysis for a number of programs and initiatives.
May assist staff or faculty members with data manipulation, statistical applications, programming, analysis and modeling on a scheduled or ad-hoc basis.
Performs other related work as needed.
Minimum Qualifications
Education:
Minimum requirements include a college or university degree in related field.---
Work Experience:
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Certifications:
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Preferred Qualifications
Education:
Bachelor’s Degree in Economics, Statistics or related disciplines.
Experience:
Previous research experience.
Technical Skills or Knowledge:
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.
Preferred Competencies
Proven ability to work effectively in small teams, demonstrating a strong desire to both learn from and mentor other team members.
Excellent oral and written communication skills.
High attention to detail and quality in data analysis and written material.
Experience with or proficiency in R, Python, and Bash scripting.
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.
Experiencing forming and testing hypotheses.
Drive to learn new programming or data analysis techniques.
Strong communication skills and the ability to break down complex technical problems.
Significant knowledge of statistics and/or empirical economics research.
Demonstrated ability to review and prioritize work independently and effectively.
Demonstrated ability to be resourceful and creative in problem-solving.
Working Conditions
Predoctoral Scholar will be based out of Chicago, IL.
Application Documents
Resume (required)
Cover Letter (preferred)
References (preferred)
When applying, the document(s) MUST be uploaded via the My Experience page, in the section titled Application Documents of the application.
Job Family
Role Impact
FLSA Status
Pay Frequency
Scheduled Weekly Hours
Benefits Eligible
Drug Test Required
Health Screen Required
Motor Vehicle Record Inquiry Required
Posting Statement
The University of Chicago is an Affirmative Action/Equal Opportunity/Disabled/Veterans and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender, gender identity, national or ethnic origin, age, status as an individual with a disability, military or veteran status, genetic information, or other protected classes under the law. For additional information please see the University's Notice of Nondiscrimination.
Staff Job seekers in need of a reasonable accommodation to complete the application process should call 773-702-5800 or submit a request via Applicant Inquiry Form.
We seek a diverse pool of applicants who wish to join an academic community that places the highest value on rigorous inquiry and encourages a diversity of perspectives, experiences, groups of individuals, and ideas to inform and stimulate intellectual challenge, engagement, and exchange.
All offers of employment are contingent upon a background check that includes a review of conviction history. A conviction does not automatically preclude University employment. Rather, the University considers conviction information on a case-by-case basis and assesses the nature of the offense, the circumstances surrounding it, the proximity in time of the conviction, and its relevance to the position.
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