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Good day, and thanks for the visit!
Following up on an 11-year career as a math professor, I’ve been providing data-driven insights to grocery stakeholders since early 2020. My industry knowledge and technical capabilities have grown to the point where I’m leaned on heavily for my expertise. My biggest strength is turning raw data into actionable decision points for the merchant and marketing teams.
I work regularly in DataBricks using Python and SQL, leverage Tableau for data viz, and can present recommendations comfortably to any audience big or small, remotely or in-person. I have experience playing well in AWS, Azure, and on-prem Linux environments.
I prefer business intelligence and being in the weeds with category managers to cranking out hard-core machine learning, but I can scratch that itch when necessary. I’m not one to model just for modeling sake, but certainly have retained what I’d learned in grad school and during my modeling days at 84.51.
Expert: Understanding customer behavior by leaning on SQL / Python derive value from point-of-sale data. Understanding business problems, storytelling, building stakeholder relationships, mentoring teammates.
Developing: Software engineering best practices, crawl-walk-run mentality.
Data Philosophy
Motto: Common sense outshines statistical significance.
Know your data inside and out. “What’s this column mean?” should have an answer or a data dictionary.
Understand your metric defintions. “How was household penetration defined?” should not be shrouded in mystery.
Be able to describe the pipeline and the exclusions. “How did you remove outliers?” must be tractable.
Huge sample sizes that lead to meaningless statistical significance should be handled gingerly. “So you’re saying this intervention was significant?” should be held up to the business-value mirror every time.
Point of View
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