Predoctoral Scholar - Machine Learning Data Scientist

Department

Harris School Pre-Docs, Temps and Non-Student Research Assistants

About the Department

Why Policy? For a better world. Why Harris Public Policy? Because when it comes to better policymaking, evidence matters. We work at the frontiers of data collection and analysis, using the latest technology and social science methods to design policies that work for society. We conduct rigorous inquiry in an environment of innovation and risk taking. We collaborate across the private, public, and nonprofit sectors, adopting new methodologies to drive better solutions. We engage with our city, our world, and our leaders every day to evaluate and test our ideas. And we lead with our passion for driving social change, knowing that passion alone does not equal impact.

Job Summary

The Harris School of Public Policy seeks applicants for a one-year Predoctoral Scholar - Machine Learning Data Scientist position. The ideal candidate will have core interests in education research. The successful candidate will work under the supervision of Professor Anjali Adukia.

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:

Minimum requirements include knowledge and skills developed through < 2 years of work experience in a related job discipline.

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

Research

Role Impact

Individual Contributor

Scheduled Weekly Hours

37.5

Drug Test Required

No

Health Screen Required

No

Motor Vehicle Record Inquiry Required

No

Pay Rate Type

Hourly

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FLSA Status

Non-Exempt

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Pay Range

$25.65

The included pay rate or range represents the University’s good faith estimate of the possible compensation offer for this role at the time of posting.

Benefits Eligible

Yes

The University of Chicago offers a wide range of benefits programs and resources for eligible employees, including health, retirement, and paid time off. Information about the benefit offerings can be found in the Benefits Guidebook.

Posting Statement

The University of Chicago is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender, gender identity, or expression, national or ethnic origin, shared ancestry, 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.

 

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.

 

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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