What I do

Current

Earlybird Software, Data Scientist

What I do at Earlybird is twofold: at the beginning of a project I gather data and munge it into a form that we can use. This can mean integrating with an API, scraping websites, or cleaning and deduping data stored in spreadsheets and inserting or updating it in our databases. I’ve even gone so far as to build a parser to automate transforming Word doc bullet points into database tables.

Over the lifespan of a project, I figure things out about this data and user-generated data and present these findings to stakeholders. Depending on the question, this has spanned the gamut from clustering users to sussing out the network relationships between different business locations, to training models that predict people’s future behavior.

Previous

Experience and Cognition Lab, University of Chicago, Lab Manager

I ran traditional hypothesis tests and other statistical analyses on experimental data collected in the lab. I also contributed to the design of experiments and programmed online experiments run on Amazon Mechanical Turk.

Behavioral Biology Lab, University of Chicago, Research Fellow

I designed a behavioral economics experiment to separate baseline risk preference from irrational risk aversion by programming a gambling game to subtly vary risk and expected values. I also stressed a portion of participants and measured their physiological levels of stress hormones to study the effect of stress on decision making under uncertainty.

Education

University of Chicago

Degree: Bachelor of Arts (2015) with general and departmental honors in Psychology; minor in French Literature. June 2015.

Honors: Lillian Gertrude Selz Prize for Academic Excellence (2012), Dean’s List (2011–’15), Phi Beta Kappa honors society (2014)

Honors Thesis, Behavioral Economics: An Exploration of Stress, Gender, and Risk Preference in Financial and Prosocial Domains

Open Source Software

Author, multicolor package, July 2018

Co-author, cowsay package, June 2018

Author, postal package, June 2018 (accepted to CRAN July 2018)

Reviewer, rOpenSci oec package, June 2018

Co-author, rOpenSci monkeylearn package, February 2018

Co-author, rOpenSci roomba package, May 2018

Contributor, beepr package, May 2018

Articles and other Contributions

Former Co-Organizer, RLadies Chicago, 2017-2018

Data Skeptic article, 2018

MonkeyLearn Sentiment Analysis: article; source code, 2018

Captain, UChicago Women’s Ultimate Frisbee Team, 2015

Talks, etc.

2018 rOpenSci unconf, Seattle, WA, May 2018

2018 class of NASA Datanauts, January 2018

Recipient of the 2018 rstudio::conf Diversity Scholarship, San Diego, CA, October 2017

RLadies Chicago, “Oktoberfest Edition: Beer-in-Hand Data Science,” Microsoft Technology Center, October 2017 Accompanying Interview at Earlybird Software, December 2017

Chicago Women’s Ultimate Summit, “Women in Chicago Ultimate Data Analysis,” Chicago, IL, February 2017

What I Use

  • R
    • Tidyverse & base
    • Web scraping, working with RESTful APIs
    • Supervised machine learning, network analysis, cluster anlaysis
    • RMarkdown for reproducible presentations
  • git, GitHub, BitBucket
  • SQL (MySQL, Postgres)
  • Some Python, some Shiny, Google Analytics
  • The bare minimum of JavaScript, HTML, CSS :)
  • Continuous integration on Travis and Appveyor
  • Unit testing with testthat, code coverage on Codecov
  • Containerized environments with Docker
  • AWS: installing R and configuring RStudio server on Linux instance; working with the S3 API