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ShahaDeven/README.md
⚡ Data Scientist & ML Engineer ⚡

Typing SVG


LinkedIn    Portfolio   


> whoami

name: Deven Shah
location: New York, USA
role: Data Scientist @ University at Buffalo
education: MS Data Science

certified: AWS ML Engineer Associate
published: 4 papers (IEEE + Springer)

> skills --list

tech = {
    "ml": ["PyTorch", "TensorFlow", "scikit-learn"],
    "data": ["PySpark", "Pandas", "SQL"],
    "mlops": ["Docker", "MLflow"],
    "cloud": ["AWS", "GCP"],
    "llm": ["RAG", "LangChain", "Agents"]
}


> cat projects.md

+ 🤖 [AI SQL Agent](https://github.com/ShahaDeven/AI_SQL_Agent)
!    Natural language → SQL | 91.7% accuracy | 0.67s latency

+ 🤖 [E-Commerce Retention Analytics]([https://github.com/ShahaDeven/AI_SQL_Agent](https://github.com/ShahaDeven/Ecommerce_Cohort_Analysis))
!    SQL + Tableau | $30.7M upside identified

+ 🤖 [SaaS A/B Testing Analysis]([https://github.com/ShahaDeven/AI_SQL_Agent](https://github.com/ShahaDeven/Saas_AB_Testing_Analysis))
!    Bayesian | Frequentist

+ 🎓 Admissions Pipeline  
!    ML system for 3K+ applications | Explainable via LIME/SHAP

+ 📈 [Rossman Sales Forecasting](https://github.com/ShahaDeven/Rossman_sales_project)
!    LSTM model | R²=0.944 | 200x inference speedup

💼 Open to: Data Scientist • ML Engineer • AI Systems roles

Pinned Loading

  1. AI_SQL_Agent AI_SQL_Agent Public

    Production-grade Text-to-SQL Agent with 91.7% accuracy on 60 queries | What-If Simulator | Hybrid BM25 + Semantic Retrieval | Self-Healing SQL | Eval Framework | Docker + CI/CD

    Python

  2. Ecommerce_Cohort_Analysis Ecommerce_Cohort_Analysis Public

    SQL analysis of 95K customers identifying $317K revenue recovery opportunity

  3. Rossman_sales_project Rossman_sales_project Public

    Production ML pipeline for Rossmann store sales forecasting using PyTorch LSTM includes REST API, monitoring dashboard, and MLflow experiment tracking.

    Jupyter Notebook

  4. Saas_AB_Testing_Analysis Saas_AB_Testing_Analysis Public

    SaaS onboarding A/B test analysis using Python, scipy, and statsmodels. Covers experiment design, statistical testing, Simpson's Paradox discovery, and sequential testing pitfalls.

    Jupyter Notebook