Salary survey, IAAA & award-winning app

Report: Data scientists command big bucks

The demand for experienced data scientists continues to far outpace the supply, yet the base salaries for most levels of data scientists remained steady or had only modest gains (1 percent to 3 percent) compared to the previous year, and those at the very top of the pay scale (level 3 managers) saw a 4 percent drop according to a recent report. The “Burtch Works Study: Salaries of Data Scientists 2016” involved 374 confirmed data sciences, 69 percent of whom were identified as “individual contributors.” “Managers” comprised the remaining 31 percent.

Not to worry; data scientists of all ranks remain well compensated. According to the report, the median base annual salaries of data scientists ranges from $97,000 to $240,000, depending on a long list of factors including experience, education, industry, location and job responsibility. And that doesn’t include bonuses.

The report notes that compared to other predictive analytics professionals, data scientists earn higher median base salaries across every job category. The difference in base salaries is largest among individual contributors, where data scientists earn from 22 percent to 39 percent more than other predictive analytics professionals. For example, individual contributors at level 1 earn a median base salary of $97,000 within data science, compared to $76,000 in other predictive analytics positions.

To download a free copy of the complete report, visit:

Innovative Applications in Analytics Award

The Innovative Applications in Analytics Award (IAAA), which recognizes creative and unique developments, applications or combinations of analytical techniques used in practice, has garnered significant interest in its brief life. The IAAA for 2016 was awarded to an MIT-led team for its submission entitled, “An Analytics Approach to the Clock Drawing Test for Cognitive Impairment,” during the recent INFORMS Conference on Analytics & Operations Research in Orlando. Fla.

The Innovative Applications in Analytics Award is the flagship competition of the Analytics Society of INFORMS. The purpose of the award is to recognize the creative and unique application of a combination of analytical techniques in a new area. The prize promotes the awareness and value of the creative combination of analytics techniques in unusual applications to provide insights and business value.

Students’ app finds shortest wait time at hospital EDs


Numerous studies have shown that patient demand on hospital emergency departments (ED) has increased exponentially, and that demand grows each year. From 2003 to 2009, the average wait time in U.S. EDs between arrival and being seen by a medical professional increased 25 percent, from 46.5 minutes to 58.1 minutes. Prolonged wait times are reported to be a central concern in EDs and are a major reason why patients leave the ED without being seen.

Until recently, there has not been a tool that helps would-be ED users – specifically those who do not arrive via ambulance – determine which nearby hospital has the shortest wait time. Thanks to an interdisciplinary undergraduate team of students from Georgia Tech, the web-based application FindED does just that.

FindED users are taken to a screen that displays hospitals within a 15-mile radius of the user’s location. Each hospital shows the wait time (based on an annually reported average), the travel time based on real-time information from Google Maps, a quality index average, and – for the state of Georgia – the major insurance providers accepted.

Prashant Tailor, a recent graduate of the Stewart School of Industrial & Systems Engineering (ISyE) and a member of the FindED team, served for several years as an undergraduate researcher for ISyE professor and INFORMS member Eva Lee. It was through his work with Lee in support of her research that Tailor came up with the initial idea for FindED.

“I was working at Grady and other hospitals,” Tailor explains, “and I noticed that there was an excessively long wait, especially at Grady. I thought, ‘I can look up wait times for other things online – like food – why can I not do this for a hospital?’”

Tailor emphasizes the importance of the team’s interdisciplinary nature as integral to the success of putting FindED together. In addition to Tailor, the co-founders include Farhan Khan (computer science major), Dale Rivera (computer science) and Tony Shu (materials science & engineering and computer science).

“The IE aspect to me is how can I look at the entire system? How do I use a data set to model a real-life system? That’s pure ISyE,” Tailor said.

The team’s project garnered considerable attention, including a front-page  appearance on Reddit and a first-place finish in the technical paper competition at the 2016 IIE Southeast Regional Conference.

“The key challenges are to ensure that the app is relevant, the information provided is objective and up-to-date, and that patients can choose what matters to them the most – how long they have to wait versus quality ranking, or insurance acceptance, etc.” says Lee, the team’s advisor. “We have already beta-tested its usability to over 60,000 users. The win definitely motivates broader dissemination.”