The TMW Center for Early Learning + Public Health at the University of Chicago develops, tests, and implements evidence-based interventions designed to promote very young children's cognitive and social-emotional development. TMW Center interventions are designed to be embedded into existing health, education, and social service systems working at scale in a given community in order to meet families where they already are. The TMW Center has a robust research and development strategy that includes further development and testing of TMW interventions; harnessing technology to support behavior change, intervention engagement, and analysis; and furthering strategies to engage parents in the TMW Center's interventions across the health, early learning, and social service sectors.
Job Summary
The job uses best practices and knowledge of data manipulation, statistical applications, programming, analysis and modeling in order to implement projects related to the TMW Center's wearable team. The Sr. Wearable Device Engineer 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.
Responsibilities
Supports the development of the TensorFlow framework, Neural Network design and custom testing developed for the project.
Improves results and writes code as needed.
Develops a more critical view of Machine Learning models' performance not only from the traditional metrics but on custom multi-dimensional metrics and adding a business perspective.
Supports work related to audio processing, featurization, and the creation of an efficient pipeline for data processing and model testing.
Collaborates with team of Data Scientists.
Defines features of audio recording to develop novel models.
Creates cutting-edge machine learning algorithms on audio datasets.
Develops scripts and code for analyses.
Supervises contributions of student Research Assistants to prepare datasets to train algorithms.
Collaborates with team and external vendors (e.g., hardware, firmware, and electrical engineers) and weigh in on requirements to run the algorithms.
Secures and maintains needed certifications to ensure proper creation and maintenance of TMW Center data systems.
Ensures secure data storage, guaranteeing regular backups and storage in compliance with HIPAA and current best practices.
Has a deep understanding of methods to analyze complex data sets for the purpose of extracting and purposefully using applicable information. May develop and maintain infrastructure that connects data sets.
Guides staff or faculty members in defining the project and applies principals of data science in manipulation, statistical applications, programming, analysis and modeling.
Calibrates data between large and complex research and administrative datasets. Guides and may set the operational protocols for collecting and analyzing information from the University's various internal data systems as well as from external sources.
Designs and evaluates statistical models and reproducible data processing pipelines using expertise of best practices in machine learning and statistical inference. Provides expertise for high level or complex data-related requests and engages other IT resources as needed. Partners with other campus teams to assist faculty with data science related needs.
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 5-7 years of work experience in a related job discipline.
Certifications:
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Preferred Qualifications
Education:
Advanced degree in Computer Science, Statistics, Mathematics, or Economics with a focus on computer science.
Technical Skills or Knowledge:
Proficiency in Python, Numpy, Pandas and Scikit-Learn.
Ability to write production-level code.
Experience with cloud resources such as Amazon Web Services (including AWS Redshift, Amazon RDS, and Amazon Aurora).
Experience using Linux.
Experience with Arduino or hardware integration.
Experience developing and implementing machine learning solutions for real world use.
Experience handling terabyte size datasets.
Application Documents
Resume/CV (required)
Cover Letter (required)
When applying, the document(s) MUSTbe 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
40
Drug Test Required
No
Health Screen Required
No
Motor Vehicle Record Inquiry Required
No
Pay Rate Type
Salary
FLSA Status
Exempt
Pay Range
$82,000.00 - $118,000.00 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. In addition to the base pay posted above, this position may be eligible for N/A
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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