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About Me

I’m a Data Scientist & Engineer with 5 years of industry experience across financial technology, auto insurance, R&D medical devices, and aerospace manufacturing. I’m an active member of the R programming community and have developed several open source packages, participated in Google Summer of Code, and NASA Datanauts.

Career Goals

I’m searching for a remote-friendly company that values creativity, diversity and openness as a Machine Learning Engineer or Data Scientist developing data products & algorithms with R & Python while mentoring junior team members. I’m looking to push my career and skills towards adapting machine learning algorithms to improve user experiences and fight fraud.


  • Graduate Analytics Certificate at DePaul University, (2015 - 2017)
    • Coursework: Intro to Programming, Data Analytics & Regression, Database Processing for Large-scale Analytics, Advanced Data Analysis, Knowledge Discovery Technologies, Programming Machine Learning Applications
  • BS in Engineering in Biomedical Engineering at University of Hartford, (2008 - 2012)
    • Coursework: Engineering Computer Applications, Calculus (1-2, Multivariable), Differential Equations, Independent Research, Engineering Design, Statics, Dynamics, Mechacnics of Materials, Biomaterials

Data Science Work Experience

  • Data Scientist 2 at Simple Finance, (Oct. 2016 - Present)
    • Lead Data Scientist for the Onboarding product team: responsible for technical mentorship, data product strategy, external data integration with APIs, experimentation and analyzing product feature launches
    • Developed an early detection machine learning model to monitor spikes in ACH returns leading to data-informed decisions for mitigating fraud loss
    • Developed internal data science tools for power analysis and data cleaning
  • Associate Data Scientist at The Hartford Financial Services, (Apr. 2016 - Oct. 2016)
  • Data Science Intern at The Hartford Financial Services, (Nov. 2015 - Mar. 2016)
    • Developed machine learning models for auto insurance that improved loss ratio estimates, drove strategic pricing changes and provided insights on competitive position
    • Enhancing the team’s data science architecture by developing an internal R package, writing technical documentation and tutorials
  • Bioinformatics Internship at the University of Connecticut Institute for Systems Genomics (UConn), (Sept. 2015 - Jan. 2016)
    • Computational and command line programming to develop a gene database for the annotation of the douglas-fir & walnut genome
  • Student Developer at Google Summer of Code, (May 2015 - Aug. 2015)
    • Developed a web application with r-shiny to automate differential expression and survival analysis of micro-array gene expression datasets from the NIH Gene Expression Omnibus

Open Source R Software


  • gramr: Provides grammar checks in RMarkdown documents
  • ttbbeer: Data package of beer statistics from U.S. Department of the Treasury (TTB)
  • shinyLP: Bootstrap components to make landing home pages for shiny web apps
  • shinyGEO: Shiny app for gene expression differential & survival analysis

More R packages and Shiny apps available at:

More projects available at:

Invited Talks


  • Course Instructor & Developer with DataCamp (Sept. 2017 - Present)
    • Currently under-contract developing a R course titled: ‘Building Big Shiny Apps’
  • Data Science Instructor (Part-time) (Oct. 2017 - Dec. 2017)

Research Publications

  • Stella Bollmann, Dianne Cook, Jasmine Dumas, John Fox, Julie Josse, Oliver Keyes, Carolin Strobl, Heather Turner and Rudolf Debelak. ‘Forwards Column’. The R Journal, Volume 9/2, December 2017. - Paper Link
  • Dumas J, Gargano MA, Dancik GM. shinyGEO: a web-based application for analyzing Gene Expression Omnibus datasets. Bioinformatics. 2016 Aug 8. - Paper link
  • Dumas J,, Feasibility of an electronic stethoscope system for monitoring neonatal bowel sounds. Connecticut Medicine, Volume 77, Number 8, pp. 467-471, September 2013. - Paper link


Technical Skills [Keywords]

  • Computational programming & machine learning with R and Python [ggplot2. dplyr, tidyverse, pandas, scikit-learn]
  • Statistical Analysis and inference [Regression, Bagging, Boosting, Ensemble, Hypothesis testing]
  • Data processing & querying with SQL [Redshift, postgreSQL]
  • Web application development with Shiny and Bootstrap [HTML, CSS]
  • Collaborative computing with GitHub and Git [Version control]