Developed and deployed machine learning models to identify fraudulent applications for loan pre-approvals and detect fraudulent credit card transactions using supervised learning methods.Utilized Python libraries such as Pandas, NumPy, SciPy, Matplotlib, Seaborn, and Scikit-learn for model development and data manipulation.Performed Exploratory Data Analysis (EDA), data cleaning, feature scaling, and feature engineering to optimize model performance.Evaluated models using cross-validation, log loss function, and ROC curves to ensure high accuracy and reliability.Worked with deep learning frameworks like TensorFlow and PyTorch for complex model development and natural language processing.Developed real-time anomaly detection algorithms, which reduced operational risks by detecting irregularities early

Designed and maintained ETL pipelines using SQL, Python, and AWS services, improving data processing efficiency.Developed SQL and HiveQL queries for data extraction and transformation, facilitating business intelligence.Collaborated with teams to ensure data integrity across platforms and sources.Partnered with cross-functional teams, including product management, engineering, and compliance, to ensure the machine learning models aligned with business goals and regulations.Developed interactive data visualizations and dashboards for C-level executives, facilitating data-driven decision-making at the highest levels of the organization.