I don't just ask what happened. I ask why it happened — and what we should do next.
I'm a Product & Data Analyst who enjoys working at the intersection of data, users, products, and AI.
I like messy problems.
The kind where the question starts as:
"Something feels wrong with this metric..."
and ends with:
"Here's what changed → here's why → here's what we should test next."
My job is somewhere in between those two sentences. 🔎
| 🔎 Investigate | 📊 Understand | 🧪 Experiment | 🚀 Decide |
|---|---|---|---|
| Ask better questions | Find the signal | Test assumptions | Ship what matters |
| Find the "why" | Connect the dots | Measure impact | Learn & iterate |
|
Funnels User Journeys Product Metrics A/B Testing User Research PRDs & Prioritization |
SQL Python Pandas Statistics Dashboards Business Analysis |
RAG Pipelines Embeddings Vector Search LLM Applications LangChain Document Intelligence |
🧠 Product
Funnels · User Journeys · Product Metrics · A/B Testing · User Research · PRDs · Prioritization
📊 Data
SQL · Python · Pandas · Statistics · Dashboards · Business Analysis · Experimentation
🤖 AI / RAG
RAG Pipelines · Embeddings · Vector Search · LLM Applications · LangChain · Document Intelligence
A dashboard is not the outcome. A better decision is.
DATA → INSIGHT → EXPERIMENT → DECISION → IMPACT 🚀
- 🧠 Product: I think in funnels, experiments, metrics, user problems, and trade-offs.
- 🐍 Data: I use SQL + Python to move from "we think..." to "the data says..."
- 🤖 AI: I'm exploring how RAG, embeddings, and LLMs can make analytics more useful and actionable.
- 🎯 Mindset: I care less about making another dashboard and more about making sure someone can make a better decision because it exists.
SQL → Insight → Experiment → Product Decision → 🚀
Two projects. Two different problems. One goal — turning information into better decisions.
|
AI-powered document intelligence system that turns a lengthy annual report into an interactive question-answering experience. Instead of manually searching through hundreds of pages, users can ask questions and retrieve relevant information using a Retrieval-Augmented Generation (RAG) pipeline. 🔍 Built With
💡 Product Problem
🛠️ Python · LangChain · Embeddings · Vector Search · LLMs 
|
End-to-end product analytics case study uncovering where customers drop, why they don't convert, and what operational factors influence retention. 🔍 Investigated
💡 Key Findings
🧠 Product Questions
🛠️ Python · Pandas · NumPy · Jupyter · Streamlit 
|
🤖 AI / RAG 📊 Product Analytics 🐍 Python 🧠 Problem Solving
|
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🔥 |
🤖 |
📊 |
One idea. One experiment. One better decision. 🚀
