Gage Rowden, M.S.

Summary

Applied Data Scientist specializing in high-throughput biological data and production analytics systems with extensive experience designing end-to-end data workflows, developing custom R packages, and building cloud-backed applications. Skilled in Python, SQL, AWS, and statistical modeling, with a strong foundation in experimental design and high-throughput data. Proven track record of optimizing data analysis pipelines, improving lab efficiency, and delivering actionable insights to drive research and operational success.

Skills

  • Python
  • Pandas & NumPy
  • scikit-learn
  • R Tidyverse
  • SQL
  • ETL pipelines
  • Time-series analysis
  • Statistical analysis
  • Experimental design
  • Data visualization
  • AWS
  • Django & REST API
  • Docker
  • Git & GitHub
  • GitHub Actions (CI/CD)

Experience

Lead Data Scientist – Priogen Corp.

Jan 2024 – present

  • Architected and deployed end-to-end data analysis pipelines to process high-throughput diagnostic datasets (up to 100 samples/week, ~240k rows of time-series data/project), reducing analysis time from ~4 hours to seconds.
  • Led full-stack development of a Python-based LIMS (PriLIMS) using Django, automating client data intake, tracking, and reporting, eliminating 10 hours/week of manual administrative work.
  • Implemented cloud-based infrastructure on AWS to support scalable SQL storage and analysis, improving system reliability and enabling secure multi-user access.
  • Introduced Git-based version control and CI workflows across projects, improving collaboration efficiency and significantly reducing deployment errors.

Researcher IV – University of Minnesota

Aug 2019 – present

  • Developed a custom R package and automation scripts to standardize RT-QuIC data analysis, reducing hours of analysis time to seconds and increasing reproducibility across collaborating labs.
  • Streamlined data processing and reporting workflows using R and Python tools, reducing turnaround time for experimental results by 99%.
  • Built and evaluated statistical models to classify diagnostic outcomes, improving disease detection confidence.
  • Standardized cross-lab analytics and built reusable analytical frameworks adopted by multiple institutions.
  • Led laboratory establishment, expansion, and workflow redesign, increasing research capacity.

Education

M.S. in Biotechnology – Texas Tech University 2018

B.S. in Biology – Texas Tech University 2016