The hard part, of course, is figuring out what that story is.
I studied economics and creative writing at the University of Chicago, where I worked for Nobel laureate James Heckman, cleaning and analyzing data from small studies of early childhood education programs. After graduating, I spent a year teaching English in France, near Lille. I returned to the States to work for Professor Alison Morantz at Stanford Law School, where I gathered, cleaned, and analyzed data for projects on:
Between stints at Stanford Law School, I received a Master of Fine Arts in fiction from the University of Georgia, and for the past year I've been teaching writing at the University of San Francisco (while doing some freelance data work on the side).
Since 2014, I've been working as a Data Analyst (now Senior Data Analyst) at the NYU Furman Center, a joint research center of NYU Law and the Wagner School focussing on housing, real estate, and urban policy. I help make sure Furman Center researchers have all the data they need, and that that data has been cleaned and processed. I've done a lot of work developing and maintaining our data process for US Census data, as well as numerous city and state datasets. I project-managed an evaluation of the accessibility of the US housing stock using data from the American Housing Survey, and I am currently the PM and data lead on a report looking at the state of renters and rental housing in America's biggest cities. Last year (and again this year), I was the primary author of a section -- focussing on neighborhood services and conditions -- of the Furman Center's annual State of New York City's Housing and Neighborhoods report, an experience which involved working with data on schools and education, crime and police, transit and transportation, and parks.
In addition to processing and analyzing data, and writing up the results, I also work a lot on visualizing our data, including many, many graphs in Excel, maps using GIS, and interactive visualizations using Javascript. I am currently developing a framework, using Python/Pelican, Highcharts.js, and D3.js, to build static microsites for Furman Center reports, using interactive figures and maps to tell data-based narratives in an engaging and dynamic way.
I am in my second semester of an MS in Data Science at NYU's Center for Data Science, and the experience has piqued my interest in data scientific, in addition to econometric, approaches to answering empirical questions. I have also become more and more interested in the role of data visualization, particularly interactive visualizations, in building compelling stories from messy datasets.
Fiction and data analysis might seem like disparate interests, but to me they both stem from a desire to understand the world: What does it mean for a coal mine to be "dangerous," why are some mines more dangerous than others? What influences the rates of injuries at workplaces, and how can we ensure both employers and employees are treated fairly? How can we tell if a house is accessible to someone with disabilities? What does it really mean to be "rent burdened"? These questions, to me, are similar to asking what motivates a character, how someone will react to hiccups and earthquakes.