A data scientist is one of the most in-demand, high-profile careers in IT today, but Tom Walsh and Alex Krowitz have been working behind the scenes in the field for years. Walsh, a research engineer and Krowitz, a senior research engineer at cloud workforce management solutions company Kronos, sift through the influx of proprietary and customer data to identify patterns and gain insights based on that data.
"We both work in the workforce management and timekeeping division here at Kronos. There are generally two kinds of projects we regularly handle; mining patterns within data to improve our own products is one and the other is taking on specific sets of customer data to gather and deliver insights from that," says Walsh.
What companies are looking for is ultimately the capability to make predictions based on that data, says Krowitz. Companies use those predictions to help drive everything from marketing strategy to resource allocation, personnel levels and staffing, or to predict retail sales, he says.
"We have products that use machine learning algorithms to help customers with these predictions. We are constantly working to refine those algorithms and make the predictions much more accurate. Customers are looking for help with things like predicting how much business there will be in a retail store, or sales volume per store, or in a hospital they want to predict how many patients will be admitted. Armed with that data, they can better understand the relationships between how many staff members they need for their business, or how to structure their supply chain, for example," Krowitz says.
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What degrees do data scientists need?
Walsh holds a Ph.D. in machine learning and worked in a series of both academic and industry jobs involving applied machine learning and robotics before coming to Kronos two years ago. Krowitz, who has a master's degree in computer science and a bachelor's degree in physics, he came to Kronos after a stint at a neural networks company.
At least a bachelor's degree is required to become a data scientist, and a master's degree is recommended, but it doesn't have to be a degree specifically in data science, says Professor Sue Metzger, an instructor in management information systems (MIS) at Villanova University. There are a number of industries and areas where a data science focus is useful, she adds.
"We offer a master's of science in analytics, and we also have a minor in business analytics or data analytics. We've been pretty aggressive getting into the data science and data analytics space, because we know how applicable this area of focus is for a lot of different careers. If you're going into marketing, you have to get that minor. It's also recommended if you're going to be a programmer," Metzger says.
Patrick Circelli, senior technical recruiter and lead technical trainer for IT recruiting and staffing firm Mondo says that data scientists are also in high demand from larger government clients in the Washington, D.C., area and its suburbs.
Demand for data scientists grows
"There's this growing need, as the data science field evolves, for predictive analysis and explanation. That's one of the largest spaces where we're filling data science roles, but we also see demand in areas like development and design companies, or software companies, especially those that have SaaS or PaaS (platform as a service) offerings that must constantly evolve their products based on user feedback and competitive analysis," Circelli says.
An MIS degree is a great foundation for a career in data science because of the focus on sources of data and the incredible reach into related areas like programming and database design, Metzger says.
"If you're thinking about being a data scientist, MIS is a great place to start. MIS [at Villanova] requires a database course and a programming course, among others. We don't expect students to become programmers, but we do expect them to understand the programming process. And a database course, because you can't do good analytics until you understand where your data is coming from. Then, you have to develop some analytics skills, including the ability to model data, to do statistical analysis, is also important," Metzger says.
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Data lost in translation?
What's also important is the ability to communicate the results of data analysis and visualization to people who don't have the same level of technical and analytics expertise, Walsh says.
"You need to have a unique combination of interests and skills -- the curiosity to want to know what the data's telling you; the hard skills to get that data, to wrangle it into a form where you can analyze it; and the ability to explain in layman's terms the results of the analysis and the context of what that means for business -- you have to repackage the output in a way that's meaningful for someone who has to act on that data," Walsh says.
Because insights and predictions gleaned from data are often used as the basis for business decisions and strategy, the need to understand and explain data analyses and to make solid predictions is almost mandatory in data science, more so than in other disciplines, Krowitz says.
"Anyone who's at the management level isn't necessarily going to want to understand the intricacies behind the data, they really just want to know why it impacts them, and how to direct business strategy because of the results. So, as a data scientist, you have to understand the business well enough to explain it to someone who's going to make very important decisions based on it," he says.
When Walsh and Krowitz themselves are hiring for a data scientist position, there are a few qualities they look for over and above technical skills and relevant background and education.
"If we're looking at a candidate who has a Ph.D., sure, we look at their education and their work background. We also look at their publication history to see what areas they've focused on and the experiences they've had to see if they'll fit. But we also want someone who loves data and who gets incredibly excited about, say, doing things like data analysis, or working out a particularly tricky business problem," Krowitz says.
As the field evolves, organizations will continue to demand skilled and passionate data scientists, and will increasingly specialize in those skills, says Mondo's Circelli.
"It's so exciting to see the field evolve -- now our clients are looking not just at hiring for general-purpose analytics, but they'll maybe hire a few people: One who solely sets up systems and algorithms to gather the data, and another to do analysis. It's that evolution and shift in demand based on changing needs that's so fascinating to see," Circelli says.
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