Alumnus Jamey McDowell (MSBA ’18), Data Scientist at iHeartMedia, in San Antonio, TX, speaks with Alumni Council member Brian Starr (’01) about adding value through analytics to business challenges.
Brian: What is the story /connection that brought you to Wake Forest?
Jamey: I went to Furman University in Greenville, SC, for my undergrad and had a few sports analytics research opportunities while I was there, including an internship at a machine learning firm. This made me realize that I didn’t have enough of a computer science background to pivot into data science successfully. I didn’t want to focus on just the technical aspect of data science and wanted a business background, as well, so that I could eventually transition into a managerial role as my career progressed. Wake Forest launched the MSBA program my senior year at Furman, and the specifics of the program piqued my interest, as did the possibility of continuing my education in the Carolinas.
Jeff Camm, Senior Associate Dean of Analytics Programs, came to Greenville and had dinner with students who were interested in analytics. As part of this engagement and learning more about the program, I became even more interested and applied shortly thereafter.
Brian: What was your experience like at Wake Forest?
Jamey: My Wake Forest experience allowed me to translate my passion for analytics and data science into a broader horizon and helped me identify how I could use those skill sets as a business professional. The MSBA program does a great job of exposing individuals to the world of analytical business problems and business use cases, without trying to make them an expert in every analytics model available in the marketplace. This program matched up well with my background in mathematics and allowed me to optimize my experience in the more technical elements of the course work.
The importance of understanding how to learn new analytics techniques and the underlying logic behind those techniques is far more valuable than learning a specific analytical model or language, as those specific models or languages tend to become obsolete quickly.
Brian: You spent almost three years at Disney. In layman’s terms, can you describe the type of work you did there?
Jamey: After graduating from the MSBA program, I landed an internship with Disney working on Disney’s cruise line dynamic pricing model. The work I was doing was like airline pricing (i.e., airline tickets are dynamically priced, as they tend to get more expensive the closer you get to the date of usage). The same is true for cruises. I worked on forecasting models to predict the demand for different price points.
After the internship, I began working full-time on different Disney projects. One of those projects was building forecasting models to project how many people would show up at the amusement parks at a level of detail that would facilitate the planning of operations at individual parks.
Using extensive datasets like FastPass booking, dining reservations, and other elements made this work engaging. For another project, I forecasted demand for media viewership across Disney’s media outlets (ABC, ESPN, etc.). The media viewership projections are critical for the marketing and selling of advertising space, driving both demand for advertising as well as the pricing of given time slots on specific platforms.
Brian: What does it mean to you to complete a good analytical endeavor as a data scientist? How do you define success given the varied nature of each work product?
Jamey: Early in your career, success criteria is typically defined for you, and the specific area of study is also somewhat defined as part of the work. As someone becomes more experienced and contextually aware of a market or industry, the onus falls on you to go out and find good use cases for study that focus on a business problem to be solved. The critical element in this process is centered around understanding of the business to put the analytical need into appropriate context. At the end of the day, success isn’t defined by the sophistication of the analytical model used, but the end business decision that was guided or facilitated by the work.
Brian: In January, you left Florida for San Antonio to work for iHeartMedia. What led to this transition?
Jamey: The transition to San Antonio and the role at iHeartMedia came at a great time for me both personally and professionally. From a professional standpoint, I was looking forward to working with an analytical team that was newer and still in the early stages of formation. At Disney, the analytics program and processes are generally well-established, which was a great way to learn the core fundamentals of practicing data analysis and analytics in a practical business application.
The opportunity at iHeartMedia was an opportunity to be part of the “ground floor” building of an analytics program and face the challenges of building the capability up within an organization.
In addition to my career interests, the opportunity to live in San Antonio appealed to me. My love of music and the opportunity to help grow an analytical organization, in tandem with my love of the social and food scene in San Antonio, made iHeartMedia a great fit for this stage in my life.
Brian: Given the extraordinary pandemic environment that everyone has been living through the past two years, how has that impacted the building of your data and analytics career?
Jamey: The biggest thing I have learned is that I’m not going to get as much time with co-workers as one would expect in a traditional office setting. The “water-cooler” talk is tremendously helpful as part of the formative stages of a project or understanding a business need. To combat that negative impact, it’s been critical to focus on getting the most feedback and engagement from the “planned” encounters to help accomplish many of the same things those traditional informal discussions would have helped drive forward.
The other place where challenges arise is just developing the general business understanding in an environment where it’s likely you aren’t talking to the business teams consistently. The ability to infer and “read between the lines” within a business takes time to develop and is something that I continually work on to help improve the applicability of my work.
Brian: How do you create the connection for the business teams to help understand the potential value of the analytical work?
Jamey: I maintain a list of things [that I can do] that would offer high value to a business and then organize those ideas into “easier to do” vs. “harder to accomplish”. As part of a conversation with the business teams, I bring these ideas forward; based upon the reactions from the team on each item, I focus on a few items that are easier to execute and provide value to help the business while building commitment from stakeholders to the analytics program.
Many times, the items that create value for the business are the items that may be the simplest from an analytical perspective, but the most challenging from a data management perspective. Most business decisions that are made daily can often be supported through building out a business relevant dataset and simple toolsets to distill the insights into something usable and consumable.
Brian: Who would you consider a mentor or someone that had a tremendous influence on you?
Jamey: I have been fortunate to have several mentors that have had a great influence on me. Dr. Camm was a tremendous influence for me at Wake Forest. Working with him on a research project helped me understand the differentiation between business value and how technically hard something is to do. Another professor, Dr. Kevin Hutson at Furman University, introduced me to the world of analytics through multiple sports analytics projects. We did a “Moneyball” project for baseball, as well as an analytics project around March Madness in college basketball. The ability to blend my love of sports with analytics really excited me about the possibilities that an analytics career could have.
My manager at Disney, Ludwig Kuznia, showed a strong interest in new approaches to modeling and was always keen to adopt technologies that could maximize the speed to market on our solutions and drive business impact.
Brian: What advice would you give other graduates?
Jamey: One of the best questions I was asked while at Wake Forest was to “explain linear regression like you’re talking to a young child”. This resonated with me, as often we are overly proud of the stuff we “know” and want to show people how “smart” we are. At the end of the day, it’s about helping another person using what we know to drive business value and make them more effective.