Analytics Specialist at Landmark Group, Dubai — working at the intersection of retail operations, data systems, and AI automation. If it's repetitive, I automate it. If it's complex, I find the signal. If it involves a spreadsheet, I've already written the SQL.
Turning 10,000-row exports into one-line answers that a director can act on before their coffee gets cold. Air freight routing, GCC compliance, inventory patterns — I find the signal in the noise.
Building LLM-powered agents that handle tasks nobody wants to do manually — compliance checks, GRN reconciliation, report generation. If it's repetitive, it deserves to be automated out of existence.
Data is the start, never the finish. I translate analysis into strategic moves — reducing client churn, recovering revenue, or building dashboards that people actually click on Monday mornings.
Every number below is from a real dataset, a real decision, and a real person who had to act on it. No simulations. No projections. Just what happened.
You think you have a stockout problem. You actually have a signal latency problem. Once you see this once, you see it everywhere.
They're impressive in demos. The hard part is making them reliable enough that a supply chain manager trusts them at 6am on a Monday.
GCC retail operates on different rhythms. Same fundamental truth though: data is only useful if someone acts on it before the moment passes.
The most useful thing wasn't CAPM. It was learning to think in probabilities — a mindset that transfers weirdly well to ops analytics.
Nobody gets excited about GRN reconciliation. Until you build something that does it automatically — then suddenly everyone wants to see the demo.
The hardest part of analytics isn't the SQL. It's building something people actually open. Here's what makes dashboards get used.
Promoted in 4 months. Building AI automation pipelines alongside core analytics work. Living the "spreadsheet to shop floor" life in the GCC.
Moved from India to the GCC. Entered one of the region's largest retail ecosystems. The scale was the adjustment — everything else was just analysis.
Started building AI tools for retail ops. The conviction: ops teams deserve software built for them, not retrofitted from enterprise tools.
Reduced churn, recovered revenue, drove Q4 growth. First time seeing how fast good analysis moves a business metric when leadership listens.
Graduated with a degree in Technology. Additionally completed the Google Data Analytics Specialization and Yale Financial Markets (With Honors) during this time.
Don't be a stranger.
Whether you're building something with AI, working in retail operations, or want to debate the best way to structure a Power BI dashboard — I'm here for it.