Senior Data Scientist

Department

BSD MED – Pulmonary – Parker Research Staff

About the Department

The Section of Pulmonary and Critical Care Medicine has been a model of exemplary patient care, research, and post-doctoral training for more than 20 years. The 30 faculty, clinical associates and research associates along with a staff of more than 72, have devoted themselves to the pursuit of excellence in each of these important activities.

Job Summary

The Senior Data Scientist will work in collaboration with Dr. Parker, a medical ethicist and health services researcher who studies the allocation of scarce medical resources. He is specifically interested in absolute scarcity problems, where demand greatly exceeds supply, and healthcare systems cannot avoid triage scenarios. He applies advanced empirical methods to evaluate and design allocation systems according to the underlying ethical principles. His current projects focus on deceased donor organ allocation, critical care/ventilator rationing under crisis standards of care, and COVID-19 vaccine allocation.

In collaboration with Dr. Parker, the Senior Data Scientist will participate as a skilled individual contributor and/or lead researcher, contributing his/her high-level expertise to each project’s development. The position will focus on the development and execution of complex data analyses and modeling projects. Databases include electronic health record datasets and national transplant registries.

Responsibilities

• Data cleaning and management. Prepares analytic datasets using data analysis software (E.g. R tidyverse packages, Pandas for Python) from electronic health care record data sets.

• Follows the standards of transparent and reproducible data science including use version control software such as git and code with a literate programming style.

• Anticipates data management needs, including identifying when data pulls will be necessary and obtaining newer versions of source national datasets.

• Data visualization with appropriate statistical software, such as the ggplot2 package in R.

• Fits complex statistical models with appropriate statistical software, such as R or Stata. Trains, tunes, and tests machine learning prediction models with appropriate statistical software, such as scikit-learn, tensor flow, or pytorch packages.

• Investigates and apply new data science methodologies to projects.

• Reports results of data analysis, prepare figures and tables.

• Draft and revise manuscripts for submission.

• Prepares grant proposals.

• Supervise medical students and other junior trainees.

• Runs weekly lab meetings and serves as point of contact for trainees and collaborating data scientists.

• Present research findings at meetings. Analyzes moderately complex data sets for the purpose of extracting and purposefully using applicable information.

• 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.

• 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 statistics/epidemiology/computer science or related field.

Experience:

• Experience with electronic health record data.

Preferred Competencies

• Knowledge of regulatory policies and procedures.

• Understanding of electronic health care record data.

• Analytical skills.

• Problem-solving skills.

• Attention to detail.

• Organizational skills.

• Superior verbal and written communication skills.

• Work independently and as part of a team.

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

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

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FLSA Status

Exempt

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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.

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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