Data Science Analyst

Updated: 3 months ago
Location: Chicago, ILLINOIS
Job Type: FullTime


BSD SUR - OHNS: Thirty Million Words - Tech

About the Department

The TMW Center for Early Learning + Public Health (TMW Center) develops science-based interventions, tools, and technologies to help parents and caregivers interact with young children in ways that maximize brain development. A rich language environment is critical to healthy brain development, however few tools exist to measure the quality or quantity of these environments. Access to this type of data allows caregivers to enhance interactions in real-time and gives policy-makers insight in how to best build policies that have a population-level impact. The ECM team within TMW Center in partnership with Schmidt Futures is building a low-cost wearable device that can reliably and accurately measure a child’s early language environment vis-à-vis the conversational turns between a child and caregiver. The goal is to provide accurate, real-time feedback that empowers parents and caregivers to create the best language environment for their children.

Job Summary

The TMW Center is looking for a Data Science Analyst to support the wearable team in the development of this wearable technology. As a member of the TMW Center’s wearable team, the Data Science Analysts will develop, test, and validate existing machine learning algorithms, train new algorithms, lead ETL efforts, and partner with other team members and vendors to connect the algorithm, hardware and firmware pieces together. The TMW Center seeks candidates who are dynamic, collaborative, and curious.


  • Define features of audio recording to develop novel models.

  • Create cutting-edge machine learning algorithms on audio datasets.

  • Develop scripts and code for analyses.

  • Analyze moderately complex data sets for the purpose of extracting and purposefully using applicable information.

  • Build and analyze statistical models and reproducible data processing pipelines using knowledge of best practices in machine learning and statistical inference.

  • Supervise contributions of specialized Student Research Assistants to prepare datasets to train algorithms.

  • Assist Tech Lead on a weak supervision pipeline.

  • Collaborate with team and external vendors (hardware, firmware, electrical engineers) and weigh in on requirements to run the algorithms.

  • Keep abreast of broader tech and data systems landscape and best practices; secure and maintain needed certifications to ensure proper creation and maintenance of TMW Center data systems.

  • Ensure secure data storage, guaranteeing regular backups and storage in compliance with HIPAA and current best practices.

  • Cleans, transforms, merges, and matches between large and complex research and administrative datasets. Plans own resources to collect, organize, and analyze information from the University's various internal data systems as well as from external sources.

  • Builds and analyzes statistical models and reproducible data processing pipelines using knowledge of best practices in machine learning and statistical inference. Serves as a single point of contact for all requests and engages other IT resources to assist as needed. May partner with other campus teams to assist faculty with data science related needs.

  • Performs other related work as needed.

Minimum Qualifications


Minimum requirements include a college or university degree in related field.

Work Experience:

Minimum requirements include knowledge and skills developed through 2-5 years of work experience in a related job discipline.



Preferred Qualifications  


  • Bachelor’s degree in Computer Science, Statistics, Mathematics, or Economics with a focus on computer science.

Technical Skills or Knowledge:

  • Experience with Arduino or hardware integration.

  • Experience developing and implementing machine learning solutions for real world use.

  • Familiarity handling terabyte size datasets.

  • Proficiency in Python, Numpy, Pandas and scikit-learn.

  • Experience with cloud resources such as Amazon Web Services (including AWS Redshift, Amazon RDS, and Amazon Aurora).

  • Experience using Linux.

Preferred Competencies

  • Ability to write production-level code.

  • Ability to handle multiple tasks and assignments simultaneously.

  • Problem-solving skills, including a strong ability to prioritize and collaborate.

  • Excellent verbal and written communication skills.

  • Proven ability to establish and stick to timelines.

  • Excellent organizational skills, including strong attention to detail.

  • Knowledge of university and research-related regulatory policies and procedures, including IRB procedures.

  • Ability to train others.

  • Initiative and independent judgment.

Application Documents

  • Resume/CV (required)

  • Cover Letter (required)

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

Individual Contributor

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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