Applied analytics and data science for business decisions
I use SQL and Python to turn real business questions into analysis that is clear, practical, and defensible.
My background is in market research, analytics, and business analysis, with earlier experience in finance and accounting. That mix shapes how I approach analytical work: define the question clearly, test the logic, validate the data, and make sure the result is something decision-makers can actually use.
Good analysis is more than writing queries or making charts. It starts with asking the right question, making assumptions explicit, and checking whether the data really supports the claim.
I’m especially interested in the part that often gets skipped: the thinking that happens before and between the code. That includes working through vague requests, messy data, and unclear definitions while still producing something useful and explainable.
Projects are pinned below. Each repo includes documentation that explains the question being answered in addition to the code.