These days, there is a nearly infinite amount of information that can be gathered, sorted, analyzed, and applied for a wide range of purposes. Many businesses have pressing questions that can be answered with data—but they don’t know how to harness and extract insights from it. Enter data science, which is the discipline that takes that data and provides answers and guidance in many areas such as manufacturing, travel and hospitality, sports management, healthcare research, or even the investigation of solutions and ideas for societal issues. For example, how can manufacturers leverage data from past purchases and supply statistics to determine a reasonable price point?
The answers to some of the biggest questions a business faces can come down to the work of data scientists. They use scientific methods, processes, algorithms, and systems to extract knowledge and create forecasts from vast amounts of structured and unstructured data, to help businesses apply actionable insights from that data across their company.
Not surprisingly, when considering the needs of businesses to answer questions that only data can provide, data science is one of the fastest growing careers of the 21st century. Business Insider says that in 2022 data science is number 2 of The 10 Job Roles That Will Grow Most in Demand in 2022 (businessinsider.com).
To learn more about the career of a data scientist, I reached out to Malori Meyer, a data scientist here at PROS.
Malori, why do you think data science is a good career? Why did you choose it?
I was a high school math teacher, so my undergrad was in mathematics, but I learned I did not want to teach long term. I had to figure out what I could do with a math degree, but initially only thought of engineering and teaching when I started college. I landed an internship at Dow chemical, where I was part of an analytics team and got a taste of data science and analytics and found myself really interested in it. I started applying to places to try to figure out what I could do. I didn't really know the term data scientist at that time. I landed at a large oil and gas company in Houston, in IT implementing a PROS solution.
It was incredibly interesting to me. I asked how can I be part of the PROS science team? I wanted to work on PROS advanced algorithms. The PROS project manager at the time told me that I had to have a graduate degree to be a data scientist at PROS. I was like, okay, I guess I’m headed to grad school, and I was applying to grad schools within two weeks. Fast forward to graduation, I came full circle and became a scientist at PROS working on those very same algorithms that inspired me.
What has the job been like?
I would say it's one of the most interesting applications of data science because we work with such a broad array of customers. There’s lots of variety and I don’t have to work on just one thing or one industry. People bring us very complex problems. The right solution may not be obvious at first. We have to figure out what we can help them with and what can bring them the most value.
You got to data science through an aptitude for math. What other skills would you say would lend themselves to a data science career?
Math is number one, for sure. There are other companies where you have to have more aptitude in some other areas, like “full stack” and deployment skills, and the coding languages, that you pick up along the way in school and on the job. When you start getting into applied math, you're going to have to learn a coding language.
Then, I’d say, what makes one scientist stand out from other scientists is the customer-facing skills. If you can be a nerd and be really good at math, and also be able to talk to clients about something as advanced as data science and get them to understand and approve of the solution, you're in the top 10% of data scientists. Internships really help teach you how you talk to stakeholders about very technical information, and if you can get that skill that will really make you stand apart.
Tell me about your graduate program.
A graduate program will give you a lot of opportunities beyond book knowledge. For example, we had a capstone where we worked with a company to solve their problems. We gained many skills that way.
I looked for programs that had a good blend of the two sides of the data science skillsets that I already described. I wanted to come out and have the technical math knowledge to be able to be successful, but I also wanted a program that would give me the opportunity to learn the business skills, how to transfer a problem into a solution, and how to communicate.
My program, through West Virginia University, is a cohort program with 25 people admitted each year. You're assigned a group of four to five people who individually have the skills that a fully qualified data scientist would need to have. So, for example, I had the math skills, someone else had the coding skills, someone else was business savvy, and someone was maybe from finance to understand profit and cost concepts. Together, we had all the right skills so that we could learn from eachother’s backgrounds and bring our own skills to the table at the same time. It was a very valuable program.
What kinds of classes did you take in grad school?
There were probably four statistics courses. There's a data visualization class. There's a full stack type class where you're learning how to scale models and use these big dataset tools. And then there are some of the softer skill classes where you're figuring out how to work in a business setting.
(Malori’s graduate program is detailed here Business Data Analytics | John Chambers College of Business and Economics | West Virginia University (wvu.edu)).
What are the things you like best about data science?
I would say you never get bored because everything that comes your way is always different. You cannot go from one company to another company and expect to do the same thing. Even if two businesses are in the same industry, they do things differently. You're figuring out how to change the solution to fit someone’s specific business needs. And at PROS, we have the opportunity to work with so many different industries and learn so many different methods. It's always a challenge and interesting to see what new opportunities come your way to work on.
What does a typical day look like for you?
Number one is our customer-facing work. I work with sales a lot. I join customer calls and talk to them about how our science works, what we offer, try to hear what their problems are, and how we would put our solution together to solve those. Or, I may be working on a research project. We may have a long-term project where we're trying to drive improvements in our algorithms, and we work on that as time allows. I also go to a lot of conferences to learn about new things that are happening in the industries so that I stay up to date on my knowledge of any new findings. I speak about my work at conferences andalso do a lot of networking with customers, potential customers, and other scientists.
You're giving a talk soon at the Sigma conference May 3 in Miami Beach. What's it about?
It's called Refining Data Creates a Crystal Ball. It’s about the powerful potential in the oil and gas industry when we refine the right data and blend the correct additives so we can build a crystal ball of insights to solve business problems. I highlight some examples of how companies can use data science to help plan and shift through market volatility and price optimally. (SIGMA 2022 Spring Conference Education (ae-admin.com)).
What do you see in the future for data science?
The buzzwords are going to be machine learning and artificial intelligence and the drive to get people to accept and embrace those algorithms. By that I mean change management: people thinking that their jobs are being taken over by a robot or computer, but that's not the case. In the next few years, we will see even more advancements in how to explain and understand machine learning and artificial intelligence so that companies can become comfortable with these technologies. For example, how we make them comfortable with the move to AI and show them the value that's there than the more standard highly explainable models like regression. How do we help customers understand and trust where the answers are coming from? PROS is already starting to lead the way on this ML and AI ‘explainability’ journey with some of our newest advancements.
If you were mentoring a college student, what would you tell them?
I would suggest a career path somewhere along the lines of mine. Get your undergrad in math and make sure that you are truly interested in making a career of it. And then go into the real world, get a job as a business analyst, do some analytics. In that first job you can see what real-world analytics problems look like. That knowledge from your first job, learning how the corporate world works, is oftentimes required to go to graduate school. There are many master's programs for which you need to have at least three years of industry experience somewhere before they feel you can really get the full value of the degree. There is such a difference between undergrad and graduate school, you are finding real-world problems and finding solutions in graduate school rather than just reading a book and taking a test.
If you’re interested in data science as a career at PROS, you can check out our job openings here.
About the AuthorMore Content by Eric Knutson