Postdoc, Children’s Mercy Kansas City

  • Postdoc
  • Data-driven clinical research on enhancing medical care

Kansas City, Kansas, USA


The Health Services and Outcomes Research division at Children’s Mercy Kansas City invites applications for a postdoctoral research scholar position in area of health data science. This is a two-year appointment (with possible renewal) to provide advanced research training for individuals with a computationally focused doctoral degree in which they will develop a portfolio of applied healthcare research and further translational research goals at the Children’s Mercy Research Institute.

Working as part of a multi-disciplinary team, the applicant will work closely with Dr. Keith Feldman, members of research institute, and Children’s Mercy’s clinical faculty to undertake an array of projects focusing on data extracted from electronic medical records, administrative records, and/or collected from large national consortium applied to the improvement of pediatric healthcare. The core of this research lies in the notion of augmentation, not automation. We are interested in answering questions and developing insights which enhance the effectiveness of those actively engaged in the healthcare system, augmenting existing knowledge rather than replacing the individual. In particular, within intensive care settings such as the NICU, where despite extensive monitoring and documentation a significant portion of the recorded information remains underutilized.

Emphasis will be on the exploration and analysis of observational longitudinal health data; focused on advancing evidence-based medicine through improved temporal representation of patient conditions, quantification of variability, and development of explainable patient models. While the scholar will be engaged in several ongoing projects, this opportunity will include emphasis on professional and scholarly development, including the opportunity to engage and lead new collaborations, develop funding proposals, and mentor junior trainees. The ideal candidate will be a self-motivated, solution-oriented thinker with a strong background in biostatistics, epidemiology, machine learning or data science.

Qualifications/Preferred Skillsets:

Demonstrated experience in applied data mining, machine learning or statistical research
PhD Computer Science, Informatics, Biostatistics or a closely related field
Experience analyzing and interpreting data from EMR/EHR and/or clinical data
Excellent written and oral communication skills with project management skills including the ability troubleshoot independent as challenges arise
Fluency in python and/or R programming languages. Preferred experience with common analytic packages (e.g., Statsmodels, Scikit-Learn, Tidyverse)
Experience with SQL and other relational database query languages
Experienced with good computing research practices (e.g., code documentation, version control, lab notebooks), experience with repositories (e.g., GitHub) a plus.
Familiarity of medical terminologies and ontologies (e.g., SNOMED, ICD, RxNorm)
Knowledge with health common data standards (e.g., OMOP)

Link to Application (if using external Application Portal):
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