STEM CAREERS W hen it comes to analytics, Polly Mitchell-Guthrie has a long track record. She is vice president of industry outreach and thought leadership at Kinaxis, a supply chain management and analytics software company. Previously, she was director of analytical consulting services at the University of North Carolina Health Care System, and she worked in various roles at SAS, including advanced analytics R&D, as director of the SAS Global Academic Program and in Alliances. Mitchell-Guthrie has an MBA from the Kenan-Flagler Business School of the University of North Carolina at Chapel Hill, where she also received her bachelor’s degree in political science as a Morehead Scholar. She is a member of the Foresight Advisory Board for the International Institute of Forecasters, has been very active in INFORMS, the leading professional society for operations research and analytics, and co-founded the third chapter of Women in Machine Learning and Data Science (now with more than 100 chapters worldwide). Here, she and others working in data analytics share their insights into what it takes to have a career in this field. Treasure Hunt VP of Industry Outreach and Thought Leadership at Kinaxis What is a data analyst? Polly Mitchell-Guthrie How can one prepare for a career in this field? The best preparation is a solid founda-tion in three legs of a stool — quantitative fundamentals like algebra, statistics and programming, organizational/business acumen, and communication and collabo-ration. Technical skills are not enough, all are equally important. What is the earning potential? 56 Diversity in Action | FALL 2022 Finding insights in data can be like a trea-sure hunt, and part of what makes it exciting is being able to unearth valuable informa-tion that otherwise would not be available without the skills needed for analysis. Making decisions is ultimately a human responsibility, but helping humans make better decisions with data is a big step above decisions made from the gut, since we are famously prone to bias in our thinking. Where is the field headed in the next 10 years? What support is available for underrepresented candidates? Many groups are interested in greater diversity in their hiring, and scholarships are available both for university programs as well as bootcamps. One resource is Data Umbrella, a nonprofit global community for underrepresented persons in data science that organizes online data science events for the community. All levels are welcome, beginners and experts. Please describe what you love about ILLUSTRATION © SERGEYBITOS -STOCK.ADOBE.COM Someone who analyzes data to provide information to make better decisions is a simple answer. Some do analysis in tools like Excel or Tableau; others write code in programming languages like Python to deploy more complex mathematical algo-rithms, but complexity of approach is less important than delivering business value with insights derived from data. Glassdoor reports that the national average salary for an entry level data scientist is $93,167 per year in United States, but top machine learning experts can earn four to five times that amount or more. What do you believe are the most exciting aspects of this career? Advances in automated machine learning and no-code or low-code tools are bringing increasingly sophisticated analytical tools to a broader range of people. This broader access democratizes the field and allows more people to access the power of analysis and better decisions.
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