Data Scientist- AI.Health4All

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  "last_changed_at": "2026-05-29T22:41:12.882Z",
  "active_status": "active"
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  "location": {
    "raw": "Chicago, IL, US",
    "city": "Chicago",
    "region": "IL",
    "country": "United States",
    "is_remote": false,
    "confidence": 0.98
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  "inferred_at": "2026-06-06T10:03:50.046Z",
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      "city": "Chicago",
      "region": "IL",
      "country": "United States",
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  "locations": [
    {
      "city": "Chicago",
      "state": "IL",
      "country": "US"
    }
  ],
  "requisitionId": 19680,
  "displayJobTitle": "Data Scientist- AI.Health4All",
  "externalDescription": " 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 development environments. -Support the integration of AI tools into ongoing clinical and translational research workflows. ",
  "postingEffectiveDate": "5/29/2026",
  "postingExpirationDate": "6/14/2026"
}