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Developed a Python-based Google search scraper to automate keyword research and URL collection. The tool extracts search-result URLs, identifies overlapping URLs across multiple keywords, and presents the results in a structured interface for efficient analysis.
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Messy Data In. Clearer Decisions Out. 📊
Over the past year, I've noticed most businesses don't struggle because they lack data — they struggle because that data never turns into a decision.
That's the gap I work in:
→ Cleaning and analyze data with Python & R
→ Turning findings into investor-ready pitch decks
→ Polishing the final write-up so nothing gets lost in translation
If your team has data sitting in spreadsheets with no story attached, or a pitch deck that needs to actually land — that's exactly where I can help.
Open to projects. Let's talk 👇
Behind every clean chart is a messy spreadsheet 🧹
This is what a real data cleaning job looks like before it becomes an elegant bar chart — duplicate rows, missing values, inconsistent formatting, all sorted out with Python and Pandas.
#collage attempt: a look inside the data analyst's actual desk, not just the pretty output.
Another late night. Building a quant trading system an agentic model where different AI agents handle research, testing and risk, and none of them is allowed to place a trade on its own.
The hardest part so far? Being honest when the results say "not yet." Most strategies I've tested failed once real costs were included. That's exactly what testing is for.
Keeping The Psychology of Money and The Diary of a CEO close while I figure it out. What are you reading these days?
Curious how you’ve separated the agents from execution. When you say none can place a trade on its own, is that enforced through a separate execution service with fixed risk checks, human approval or both? I’d love to understand where the agent's decision-making ends and the hard rules take over.