Jillian Payne with Contributions from Sandy Steiger
Jillian Payne is director of data science at 84.51°. She holds a Bachelor of Science in business, marketing, and decision sciences from Miami University, Ohio.
Statistics is one of the fastest-growing fields today. In the age of information, it seems like almost every organization is looking to hire more data scientists to help them manage the ever-increasing stockpiles of customer data.
I remember hearing a prediction eight to 10 years ago that, before long, the supply of data scientists would not be able to keep up with the demand. As a hiring manager of data scientists, I can say this prediction has come true. Besides the sheer demand, it is also a challenging and rewarding field I would recommend to anyone with a knack for numbers to pursue. However, the continued success of our field doesn’t rely solely on understanding the latest models or programming language—it’s about being able to communicate the takeaways of our findings to effect meaningful change to sometimes longstanding business practices.
Working in statistics is unique because, while it is technically a science, there is a degree of art required to execute your responsibilities properly. I recently moderated a panel discussion on business applications of machine learning and AI and noticed a consensus from other hiring managers. Their ideal candidates weren’t just well-versed in data science; they also had a background/hobby in art or music, which demonstrates creativity. These managers found that these candidates were able to provide more creative ideas about how to approach a problem, visualize a solution, and deliver functional results. Just because a model gives you an answer doesn’t mean it always makes sense. When you think about the situation you’re applying insights to, there is a lot of art and creativity that goes into developing the perfect solution.
The main advice I would give to someone starting a career in statistics fresh out of school would be to take a step back and understand that the rules are different in the business world. Most of the people we hire don’t have business degrees. They enter into our organization full of excitement to build and apply the highly sophisticated models they learned about in school but often fail to first do the more mundane work that shows the value of what the models do. As an employee of a business, instead of a researcher for a university, you have to remember your core function is to drive value for your organization. And if you get to use really cool statistical models along the way, then that’s a bonus!
Build your business skills. Spend a lot of time at the beginning of your career just actively listening to really understand the business context of what you’re being asked to accomplish. With our training, it’s easy for us to understand the very technical issues facing us but not always the specific business objectives. Many times, when we miss the mark on a deliverable, we didn’t understand the context from the beginning. Ask questions and repeat what you hear to ensure you understand correctly. Remember, it’s not about using tech jargon; it’s about speaking in words business leaders understand.
Statistics alone will not do all the work for you. As a statistician, it’s up to you to make the connections. And you won’t always have actual output at the end of the day. Sometimes, you have to think more holistically and strategically to continue to advance underlying statistical models used by your organization. And don’t be afraid to use the artistic side of your brain to find solutions that might be outside the box.
Thank you for sharing! I’m fresh out of school and in my first week on the job where I’ll be meeting with clients on a regular basis. It is heartening to hear that more hiring managers are like mine in that they value not only technical capability but essentially customer service skills and the ability to work with clients to produce analyses in context.
I have found using visualizations is a big part of the “art” of articulating the science to the business.
Checkout https://pyviz.org/. It’s a rapid prototyping opensource visualization tool for Python.