I'm a data professional at Capgemini with 3+ years navigating the full data stack — from writing SQL queries at midnight to shipping GenAI pipelines by morning. My path is deliberate: Analyst → Scientist. Every project I take on brings me one model, one insight, one experiment closer.
My data story starts in 2018 when I chose to specialize in Data Science within Computer Engineering at Presidency University. That decision wasn't accidental — I was already obsessed with the idea that numbers, if listened to carefully, could narrate the truth about any system.
"I didn't choose data. Data chose me — through every dashboard that revealed something no one expected."
At Capgemini, I've worn many hats: Operations Analyst, Data Visualization Engineer, AI Automation Builder, and CreditLens Implementation Lead. Each role deepened my conviction — data is only powerful when it drives action.
My next chapter is Data Science: building models that don't just describe what happened, but predict what's coming. I'm actively sharpening my skills in deep learning, statistical modeling, and advanced GenAI pipelines — because the analyst in me wants to become the scientist.
Outside work, I'm the kind of person who reads ML papers for fun, experiments with LangChain on weekends, and finds genuine excitement in a well-tuned confusion matrix.
Every role has been a deliberate step — not just climbing a ladder, but expanding the radius of what I can do with data. Here's how the story unfolds.
Every certification here was earned with intent — not just for a badge, but to close a specific knowledge gap. AWS and Azure prove cloud readiness. IBM and DataCamp prove hands-on ML. CreditLens proves domain depth.
I'm actively looking for my next challenge — ideally somewhere that values both analytical rigor and creative problem-solving. If you're building something interesting in data, ML, or AI, I want to hear about it.