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.
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.
- 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.
Optimized PL/SQL queries and tuned database performance for NBT Bank.
Led offshore support team and implemented real-time/batch data solutions.
- Optimized PL/SQL queries and tuned database performance for NBT Bank.
- Led offshore support team and implemented real-time/batch data solutions.
Provided back-office and database support services.
- Provided back-office and database support services.
Developed an end-to-end machine learning pipeline to detect fraudulent transactions using Python.Performed data cleaning, handled missing values, and managed class imbalance using SMOTE (Synthetic Minority Over-sampling Technique).Applied Standard Scaler and conducted EDA (Exploratory Data Analysis) using heatmaps, histograms, and violin plots.Engineered features like transaction frequency, time since last transaction, and rolling stats.Used Recursive Feature Elimination (RFE) for feature selection.Trained Logistic Regression, Random Forest, and K-Nearest neighbours.Evaluated models using Precision, Recall, F1-Score, AUC-ROC, and Confusion Matrix.Tools Used: Python, Scikit-learn, Pandas, Matplotlib, Seaborn
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