Aspiring Data Scientist | PL/SQL Developer with 10+ Years of Experience
Analytical and detail-oriented professional with 10+ years of experience in PL/SQL development in the banking domain, now transitioning into Data S…
Aspiring Data Scientist | PL/SQL Developer with 10+ Years of Experience
Analytical and detail-oriented professional with 10+ years of experience in PL/SQL development in the banking domain, now transitioning into Data Science. Completed hands-on projects in machine learning, data preprocessing, and model evaluation. Strong foundation in Python, Scikit-learn, and data visualization. Seeking to apply analytical skills and domain knowledge in data-driven roles including Data Analyst, ML Engineer, or Junior Data Scientist.
<p>Provide PL/SQL development and support for banking clients like Karnataka Gramin Bank, Bandhan Bank, and Kotak Mahindra Bank.<br>
Handle daily production jobs, SLA-based issue resolution, and automation scripts.<br>
Collaborate with DEV (Development)/UAT (User Acceptance Testing) teams to validate enhancements and resolve incidents.<br>
Domains worked on: NEFT/RTGS, AML (Anti-Money Laundering), CIF, credit interest processing.</p>
- Provide PL/SQL development and support for banking clients like Karnataka Gramin Bank, Bandhan Bank, and Kotak Mahindra Bank.
- Handle daily production jobs, SLA-based issue resolution, and automation scripts.
- Collaborate with DEV (Development)/UAT (User Acceptance Testing) teams to validate enhancements and resolve incidents.
- Domains worked on: NEFT/RTGS, AML (Anti-Money Laundering), CIF, credit interest processing.
<p>Optimized PL/SQL queries and tuned database performance for NBT Bank.<br>
Led offshore support team and implemented real-time/batch data solutions.</p>
- Optimized PL/SQL queries and tuned database performance for NBT Bank.
- Led offshore support team and implemented real-time/batch data solutions.
<p>Provided back-office and database support services.</p>
- Provided back-office and database support services.
<ul><li>Developed an end-to-end machine learning pipeline to detect fraudulent transactions using Python.</li><li>Performed data cleaning, handled missing values, and managed class imbalance using SMOTE (Synthetic Minority Over-sampling Technique).</li><li>Applied Standard Scaler and conducted EDA (Exploratory Data Analysis) using heatmaps, histograms, and violin plots.</li><li>Engineered features like transaction frequency, time since last transaction, and rolling stats.</li><li>Used Recursive Feature Elimination (RFE) for feature selection.</li><li>Trained Logistic Regression, Random Forest, and K-Nearest neighbours.</li><li>Evaluated models using Precision, Recall, F1-Score, AUC-ROC, and Confusion Matrix.</li></ul><p></p><p>Tools Used: Python, Scikit-learn, Pandas, Matplotlib, Seaborn</p>
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