Data Analyst turning shop-floor reality into numbers you can trust.
MSc Data Science
Distinction
SQL · Power BI
Python · Excel
Open to Data Analyst roles
Most analysts meet data on a screen. I met it on the shop floor. Two years at Tesco and now M&S, where every cancelled order, empty shelf and long queue becomes a number in someone's report.
Every receipt tells two stories. One is what the customer bought. The other is everything that went right or wrong to get it there. I learned to read the second one.
An MSc in Data Science with Distinction, earned while working shifts. Then a six-month internship at Atheon Analytics, writing SQL on a live database and reconciling the sales, revenue and cost figures the business reported on.
AI can write a query in seconds. It can't tell you which number is wrong, or why.
One bar is wrong. Keep scrolling to fix it.
SMALL NUMBERS.
BIG DECISIONS.
Don't take my word for it. Click a question and run the query on my retail project (sample retail dataset, 15,000+ rows).
-- pick a question above to run a query
"Can we trust this data?" SQL ETL on a live database with checks at every step. "Why does this report take so long?" A live Power BI dashboard: about 30% less reporting time. "Why don't these numbers match?" Sales, revenue and cost figures reconciled and traced to the source.
Bake too much: waste at closing. Bake too little: an empty shelf at lunch. Every day I see how close the forecast gets to reality.
Online orders, tills and stock in a team of 10+, where one missing item means a cancelled order. That question became my retail project.
Python scripts combining 3+ systems (about 25% less manual work) and SQL optimised on 100,000+ records.
Graduation · MSc Data Science, Distinction1 in 13 online orders never reached the customer. Why?
From raw retail data to a tested, trusted model, then to a decision.
