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About

Data Analyst and AI Engineer with expertise in SQL, Python, and statistical modeling finishing a B.Tech in CSE (AI & ML) at VIT Bhopal. Experience includes engineering ML data models at Taj Cement that reduced manual analysis processing time by 20% across 3 business units.

Experience1

  1. IT Intern

    May 2026 — June 2026

    Taj Cement (Hills Cement Company Ltd.)

    Analyzed 100,000+ operational records across 3 internal departments, identifying key bottlenecks and improving workflow throughput by 15%. Implemented Random Forest models and time-series anomaly detection algorithms, cutting manual data processing time by 20%. Engineered SQL pipeline scripts used by 4 cross-functional teams, reducing operational query retrieval latency by 25%.

Projects3

  • AI Career Intelligence

    Architected relational SQL schemas to clean and structure 25,000+ unstructured job postings and candidate profiles. Executed Exploratory Data Analysis (EDA) with TF-IDF vectorization and cosine similarity, achieving an 88% precision score in skill gap identification. Optimized SQL window functions and aggregation queries, increasing candidate profile parsing speed by 35%.

    • SQL
    • Python
    • Jupyter Notebook
    • Pandas
    • Scikit-learn
    • NLP

  • Fraud Detection & Financial Risk Analytics

    Queried 50,000+ financial transaction logs using complex SQL JOINs to isolate top 5% high-risk financial anomalies. Applied SMOTE oversampling techniques to resolve a 95:5 class imbalance, elevating model recall rate from 62% to 91%. Trained Isolation Forest and Logistic Regression models, reducing false-positive financial risk flags by 18%.

    • SQL
    • Python
    • Jupyter Notebook
    • Pandas
    • Seaborn
    • Risk Modeling

  • Smart Retail Analytics Platform

    Built an automated retail pipeline aggregating 10,000+ customer transaction logs, inventory metrics, and revenue ratios. Executed RFM (Recency, Frequency, Monetary) SQL queries to segment 4 customer tiers, identifying top 12% high-value revenue drivers. Designed interactive dashboards tracking 8 key retail KPIs, improving stock demand forecasting accuracy by 22%.

    • SQL
    • Python
    • Jupyter Notebook
    • Customer Segmentation
    • Data Visualization

Education1

  • B.Tech

    2023 — 2027

    VIT Bhopal University

    Computer Science