Data Analyst in Machine Learning and Healthcare

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Data Analyst in Machine Learning and Healthcare

We are seeking a full-time staff Data Analyst to perform statistical programming and analyses of medical image data related to a broad scope of investigational research in brain and lung imaging.


The data analysis joins a young and vibrant group of researchers who are developing novel deep learning methods for real clinical applications. S/he will provide support for the group, attend group meetings, and has the opportunity to become engaged in the research and learn new techniques in machine learning.  The applicant is expected to have strong computational and analytical skills and be comfortable with the Python programming language. Expertise using statistical programming in R is required, and familiarities with statistical inference is highly desirable.



  • Bachelor’s degree in Computer Science, Electrical Engineering, Biomedical Engineering, Mathematics, Statistics, Economics or comparable field required, Master’s preferred.
  • Experience with Python programming language; experience in additional languages (R, MATLAB) is a plus.
  • Familiarity with one of the deep learning frameworks is a plus (PyTorch or Tensorflow). Experience with medical image data analysis is highly desirable.
  • Familiarity with a broad range of statistical methods such as parametric and non-parametric tests of significance, linear and logistic regression; some longitudinal analysis experience is desirable.


About Our Team

We are uniquely positioned at the intersection of the largest health care system in the US, the University of Pittsburgh Medical Center (UPMC), and top-ranked academic institutions, the University of Pittsburgh (Pitt), and the Carnegie Mellon University (CMU). Our team consists of machine learning researchers (postdocs and graduate students) as well as clinicians who have complementary expertise. We develop novel deep learning and other machine learning methods for application to challenging clinical problems. We are well funded by NIH, NSF, industry, and internal institutional grants.

If interested, contact Kayhan Batmanghelich, Ph.D. at For details of ongoing research work, visit