Research Data Scientist Intermediate
Christopher Brown is an experienced data scientist, manager, and trainer with a background
spanning educational assessment, microbiome analysis, research into federally funded awards,
and numerous projects touching upon many other domains.
Brown is a member of the ARC Scientific Computing and Research Consulting Services team
and is ready to help researchers leverage ARC services. He mentors and consults with students,
faculty, staff, and other researchers in using R, Python and other data science tools. In addition to
implementing data analysis pipelines, he provides guidance and instruction in best practices for
their implementation. He has prepared instructional materials across several domains and
programming languages, and delivered hands on and video instruction at training workshops to
audiences of varied skill levels.
In working with several biomedical research labs at the University of Michigan Medical School,
he has analyzed 16S rRNA sequencing data to aid in researching the role of the human
microbiome in sickness and health. He has also developed analytic workflows using clinical data
in sepsis, hyperoxia, and COVID-19. He created an R package to facilitate microbiome analysis
workflows, and an SOP which formed the basis for two presentations at two post-graduate
courses at the American Thoracic Society’s 2021 and 2023 conferences.
While at the Institute for Research on Innovation and Science (IRIS) at the Institute for Social
Research, he taught visualization, topic modeling, and essential Python skills at several Capacity
Building workshops designed to enhance the capabilities of researchers in education and social
science. Additionally, he regularly provided support to researchers by troubleshooting code,
software, or other issues, and developed code for improving data quality, adding new features,
and linking datasets. He also created novel topic modeling workflows in Python, using cutting
edge techniques such as SciBERT.
He is skilled in project management, leadership, critical thinking, developing analytic
workflows, R, Python, SQL, training, and providing technical support. His data science tool set
also includes RStudio, Jupyter, Anaconda, Git, Shell, Spark, and Azure Machine Learning
Christopher completed the Master of Applied Data Science at the University of Michigan School
of Information in August 2022.