I'm a Data Analyst (AI/ML Engineering graduate) who loves turning messy data into clear answers: SQL data warehouses, exploratory analysis, dashboards, and ML-powered insights that a business can actually act on.
My long-term goal is to become an AI Engineer, so alongside analytics I build with NLP, transformers, recommender systems, and RAG / agentic applications, and I ship them with proper engineering (tests, CI/CD, Docker).
- 📊 Designed a Medallion-architecture (Bronze → Silver → Gold) SQL data warehouse that surfaced $29.4M in total revenue insights from bike sales data.
- 🧠 Built and deployed ML apps: sentiment analysis, emotion detection, and recommender systems with clean, tested, documented code.
- 🤝 Made my first open-source contribution to MLflow (documentation PR #23551).
- 🎯 Currently: working as a Data Analyst - Apprentice, while building toward Data Scientist → AI/ML Engineer.
| SQL Sales Data Warehouse & Analytics | Pizza Sales Analytics Dashboard |
|---|---|
| End-to-end warehouse using Medallion architecture (Bronze/Silver/Gold), with analytics on bike sales data uncovering $29.4M total revenue insights. Stack: SQL ETL Data Modeling |
Interactive dashboard analysing pizza sales trends, top products and peak periods to support business decisions. Stack: Dashboarding KPIs Data Visualization |
| SegmentIQ - Customer Segmentation | Career-Guide App |
| Customer segmentation project turning raw customer data into actionable groups for targeting. Stack: Python Pandas Scikit-learn |
Career guidance web app deployed live on Render. Stack: Python Web App Render |
| E-commerce Review Sentiment Analyzer | Emotion AI App |
|---|---|
DistilBERT sentiment classifier on real Amazon reviews, with WeightedTrainer for class imbalance, CI/CD and Docker.Stack: PyTorch DistilBERT Streamlit GitHub Actions Docker |
DistilRoBERTa emotion detection with a layered architecture, 24 unit tests and interactive Plotly visualizations. Stack: DistilRoBERTa Plotly Pytest Streamlit |
| Enterprise AI Assistant (Multi-Agent RAG) | Transformer Playground |
| Multi-agent RAG assistant (research, retrieval, SQL, report, validation agents) with a FastAPI backend, Streamlit UI and JWT auth. Stack: FastAPI Pinecone FAISS AWS Bedrock MySQL |
Seven Hugging Face tasks in one app with lazy model loading and a Dockerfile. Stack: Hugging Face Transformers Docker |
| CineMatch - Movie Recommender | Folio - Book Recommender |
| Content-based recommender on the TMDB 5000 dataset with OMDb API enrichment. Stack: Cosine Similarity Streamlit APIs |
Book recommender using CountVectorizer + cosine similarity. Stack: Scikit-learn Streamlit |
flowchart LR
A[🗄️ Raw Data] --> B[🧹 Clean & Model with SQL / Python]
B --> C[🔍 EDA & Insights]
C --> D[📊 Dashboards & Storytelling]
D --> E[🤖 ML / NLP Models]
E --> F[🚀 Deploy: Streamlit / Flask / FastAPI / Docker]
F -. Feedback .-> B
style A fill:#0d1117,stroke:#66fcf1,stroke-width:2px,color:#e6f1ff
style B fill:#131a2a,stroke:#8b7bff,stroke-width:2px,color:#e6f1ff
style C fill:#1a1230,stroke:#ff7bf1,stroke-width:2px,color:#e6f1ff
style D fill:#112233,stroke:#7df9ff,stroke-width:2px,color:#e6f1ff
style E fill:#102d2d,stroke:#4df0b5,stroke-width:2px,color:#e6f1ff
style F fill:#20152b,stroke:#f7b267,stroke-width:2px,color:#e6f1ff
- Data Warehousing: Medallion architecture, ETL, data modeling, analytical SQL.
- Analysis & BI: EDA, KPI design, dashboards, business storytelling.
- Applied ML / NLP: Transformers, recommender systems, class-imbalance handling, evaluation.
- Engineering Practices: Unit tests, CI/CD with GitHub Actions, Docker, clean README documentation.
| Stage | Focus | Status |
|---|---|---|
| Now | Data Analyst (Apprentice): SQL, Python, dashboards, business insights | 🟢 In role |
| Next | Data Scientist: statistics, ML modeling, experimentation | 🟡 Building |
| Long-term | AI / ML Engineer: LLMs, RAG, agents, MLOps at scale | 🔵 Goal |
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Data Analyst - Apprentice - Colt Technology Services | July 2026 - Present Working with real business data: SQL querying, analysis, dashboards and reporting.
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Data Science Intern - Take It Smart (OPC) Pvt Ltd Hands-on work across the data science workflow: data handling, analysis and modeling.
- 🎓 B.E. in AI & Machine Learning Engineering - CGPA 9.07 / 10
- 🏅 Best Performer recognition
- 🪟 Microsoft Applied Skills credential
- 🌐 Certifications from Microsoft, Cisco, Coursera and more
- 🤝 Open-source: documentation contribution to MLflow (PR #23551)
I'm working as a Data Analyst - Apprentice and always happy to talk about data, analytics and AI, or to collaborate on projects.
> Turning data into decisions today.
> Building intelligent systems tomorrow.