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Data Scientist- AI.HealthAll

Job in Chicago, Cook County, Illinois, 60290, USA
Listing for: University of Illinois at Chicago
Full Time position
Listed on 2026-10-05
Job specializations:
  • Research/Development
    Data Scientist
  • IT/Tech
    Data Scientist, Machine Learning/ ML Engineer, AI Engineer (Applied/Software)
Salary/Wage Range or Industry Benchmark: 100000 - 130000 USD Yearly USD 100000.00 130000.00 YEAR
Job Description & How to Apply Below
Position: Data Scientist- AI.Health4All

About the University of Illinois Chicago

UIC is among the nation’s preeminent urban public research universities, a Carnegie RU/VH research institution, and the largest university in Chicago. UIC serves over 34,000 students, comprising one of the most diverse student bodies in the nation and is designated as a Minority Serving Institution (MSI), an Asian American and Native American Pacific Islander Serving Institution (AANAPSI) and a Hispanic Serving Institution (HSI).

Through its 16 colleges, UIC produces nationally and internationally recognized multidisciplinary academic programs in concert with civic, corporate and community partners worldwide, including a full complement of health sciences colleges. By emphasizing cutting‑edge and transformational research along with a commitment to the success of all students, UIC embodies the dynamic, vibrant and engaged urban university. Recent “Best Colleges” rankings published by U.S. News & World Report, found UIC climbed up in its rankings among top public schools in the nation and among all national universities.

UIC has over 300,000 alumni, and is one of the largest employers in the city of Chicago.

This position is intended to be eligible for benefits . This includes Health, Dental, Vision, Life Insurance, a Retirement Plan, Paid time Off, and Tuition waivers for employees and dependents.

Salary:
The budgeted salary rate for the position is $,000

Position Summary
The Data Scientist will lead and contribute to the design, development, and implementation of artificial intelligence (AI) and machine learning (ML) solutions that advance healthcare delivery, clinical decision‑making, and population health research. This role integrates data science, software engineering, and translational research, with a focus on reproducibility, fairness, and real‑world clinical impact. The data scientist will serve as a technical and scientific lead, collaborating with faculty, clinicians, and data engineers to deliver impactful applied research projects.

The position also includes mentorship of junior researchers and fostering a high standard of technical and scientific excellence across the program.

Duties & Responsibilities

  • Research and Technical Leadership
  • -Lead applied AI and ML research projects in healthcare and biomedical domains.
    -Develop, fine‑tune, and evaluate advanced machine learning models, including deep learning, large language models and
    -multimodal modeling for large scale clinical and biomedical data. large language models and multimodal architectures.
    -Translate research findings into deployable systems, decision‑support tools, or analytical frameworks.
    -Conduct external validation and real‑world evaluation of machine learning models using diverse clinical datasets.
    -Ensure all research outputs adhere to best practices in reproducibility, documentation, and data integrity.
  • Collaboration and Grant Development
    -Collaborate with clinical investigators and interdisciplinary teams to identify opportunities for AI‑driven solutions.
    -Contribute to manuscripts, technical reports, and grant proposals, and institutional pilot mechanisms.
    -Present project outcomes in academic and professional settings.
    -Mentorship and Supervision
    -Supervise and mentor junior data scientists, research associates, and students.
    -Provide guidance on experimental design, software development, and machine learning methodology.
    -Promote a collaborative and transparent research environment.
  • Infrastructure and Innovation
    -Develop and maintain data processing pipelines, model repositories, and evaluation tools.
    -Implement scalable and secure computing environments that comply with institutional data‑governance and privacy policies.
    -Develop reproducible data pipelines and collaborative model…
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