We are looking to hire a life-long learner interested in developing statistical and computational tools to sift through large amounts of data with the ultimate goal of making discoveries that can make a difference in the health of people. Methods and resources created in our lab are being used by researchers around the globe. We continue innovating and providing user-friendly and statistically efficient tools to get the most out of the ever-increasing amount of data. We partner with large consortia and biomedical researchers to find the most pressing questions in the field and apply our statistical expertise to develop reliable and efficient approaches to answer them.


  • Curiosity, passion for science, and love of learning
  • A degree in computational biology, bioinformatics, statistics, or other quantitative disciplines with an interest in applying skills to biomedical research. Or working toward such degree
  • Willingness to explore and adopt new ideas and technologies
  • Analytical, statistical, quantitative, and computational/programming skills
  • R and/or python.


  • Comfortable with large data sets, distributed computing, and databases
  • Experience in cloud computing, HPC workflows
  • Experience with SQL (Postgres, bigquery, etc)
  • Reproducible research tools (snakemake, nextflow, github)


  • A collegial, inclusive, and stimulating research environment
  • Personalized mentoring to help you achieve your potential
  • State of the art data and computational infrastructure
  • Flexible remote arrangement, as needed
  • To find out more email Hae Kyung Im (haky@uchicago.edu)

Author's bio

Hae Kyung Im develops quantitative and computational methods to uncover hidden patterns in data, which can be translated to benefit people. After trying out physics, manufacturing, information security, and finance, she found a home in academia in the intersection of statistics, genomics, medicine, and big data analytics. She is currently an Assistant Professor in the Section of Medicine, Department of Medicine at the University of Chicago.


Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The source code is licensed under MIT.

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For attribution, please cite this work as

Haky Im (2020). Genomic Data Scientist Opening. IM-Lab. /job/genomic-data-scientist-opening/

BibTeX citation

  title = "Genomic Data Scientist Opening",
  author = "Haky Im",
  year = "2020",
  journal = "IM-Lab",
  note = "/job/genomic-data-scientist-opening/"