Working his way up

Jamie Elliott, Project Lead at the Health Resources and Services Administration (HRSA), started in the government ordering supplies at age 18.

1. What is your current job title and what are your main responsibilities?

Project Lead — Mapping, Analysis, and Tracking. I am responsible for a project that uses agency data to prove the success and tell the stories of our bureau’s grant programs, which increase and improve the health professional workforce in the nation’s high need areas. This position sits in the Bureau of Health Workforce at the Health Resources and Services Administration (HRSA), an operating division of the U.S. Department of Health and Human Services (HHS).

2. How do you use data science in your job? How has data transformed your agency?

The data collected by our programs are used in geographic information systems and visualization software to help understand the physical paths of our investments. These data are also used for predictive analyses and data models to foresee workforce shortages or surpluses.

“Our total application review time was reduced by 40–50%, saving our organization more than $500,000 a year.”

3. How many other data scientists do you work with?

The Bureau of Health Workforce funds the National Center for Health Workforce Analysis, which is a team of six or seven data scientists. However, the project I lead was created in our front office and our paths don’t yet intersect, but I believe they will soon. I do rely on them heavily for their insight into health trends.

4. How have you seen data science improve outcomes in your department or team?

Our bureau is constantly discovering more useful data, which marries to existing sets we own. One of the largest successes I have seen in this discovery is the evolution of processes for efficiency gains, cost savings, and increases in program integrity. For example, after discovering a large dataset within another federal agency that could benefit our loan repayment programs, I led a project to marry our application system’s data with the National Student Loan Data System at the U.S. Department of Education. I did this through the innovation program in the HHS IDEA Lab, the Ignite Accelerator. When the project was completed, our applicants saw a reduced application time of five days, and our total application review time was reduced by 40–50%, saving our organization more than $500,000 a year.

“I technically haven’t gotten a job ‘out of college’ yet”

5. What was your first job out of college?

Funny story… I am actually four weeks away from earning my first degree, a bachelor of science in web design. I started in the federal government at age 18 as a GS-03, ordering supplies. I was fortunate to work with people who believed in teaching me as much as I was willing to learn. I moved to a few different jobs through my 12 years in the government, constantly seeking new things to learn and tools to use. I eventually became a branch chief, supervising a small team before moving into project management and pure analytics. I performed analysis as a branch chief, but managerial tasks didn’t allow for 100% data science activities. So I technically haven’t gotten a job “out of college” yet, but I have had an incredible experience in my professional career thus far.

6. What were some key moments/jobs that lead you to your current role?

In 2006, I was still considered a temporary employee and was trying to become permanent. Lamenting with a fellow intern, a phone call was made by him and a few days later I had an interview at a new agency. My new supervisor took a chance on me, and constantly gave me new tasks as long as I didn’t say there was too much on my plate. I remember one day she said to me (knowing I had spent a few years in more physically laborious jobs), “You get paid to think now.”

When I changed to a junior analyst role at HRSA, we were a team of only four federal staff and 50 contract staff, developing software in a faster-paced environment than I had ever worked. This was 2010, in the heat of the Recovery Act funding, and on the eve of the Affordable Care Act. There was so much to do and because of our structure, I learned to work like our brilliant contractors, who are unbelievable at solving problems creatively. I also had two fantastic supervisors who, no matter how busy we were, were always willing to listen to a potentially innovative solution. They gave me and others space to explore options and constantly taught and challenged me. I grew so much in my five years with them.

7. What are 3 traits that you would consider to be the most important traits for a data scientist to possess?

First — Willingness: willingness to learn, willingness to ask for help, willingness to ask yourself “What if I’m wrong?”

Second — Currency: stay up on trends. This becomes harder as tools are developed more quickly (and as we get older), but one must never stop trying.

Third — Personification: I believe it is of utmost importance that I force myself into situations where I understand underlying, individual stories beneath the data with which I’m working. For example, half of my family is in Germany. I read a lot about the current refugee crisis and the hundreds of thousands of people moving at any given time. It is so easy to speak of them as numbers. But in my couple of visits this year, I went to local events where we had coffee and cake with refugees and just talked. Sometimes they shared stories of their past. This is an incredibly humbling and humanizing experience. Selfishly, this gives my work more importance; selflessly, this makes me want to work harder and do more to help those less fortunate.

“There is no wrong way to begin, except refusing to begin.”

8. How would you recommend someone get into the field of data science?

Dive in. Your first week, set aside 20 minutes (actually set a timer) where you read articles on data science, download apps, or watch lectures on YouTube; the next week increase to 30 minutes. When you get to an hour sign up for a data science MOOC. Waste no time. There is no wrong way to begin, except refusing to begin.

9. What do you think is the future of open data?

My hope is that we will see even more standardization of common identifiers to allow for amalgamation of data from different sources. We all continue to build systems for our specific needs and collect data unique to those processes. Perhaps in the future, those data could be married with another dataset to tell a completely different story. And this can absolutely be done while protecting privacy, if a concerted effort is made.

10. Do you have any additional thoughts or comments below that you would like to share with our readers?

To quote a couple great New York City classic movies: “The most valuable commodity I know of is information,” and “With great power, comes great responsibility.”

This interview was facilitated by Data Society and conducted as part of the Data in Action series, which aims to highlight the many paths of data science in the government. It was done in partnership with the Department of Commerce and the Data Cabinet chartered by the GSA.