Detail-oriented Operations Associate Analyst with a strong background in data science and quantitative analysis. Experienced in managing large datasets, building automated data pipelines, and creating interactive dashboards to track operational metrics and Key Performance Indicators (KPIs). Proven ability to collaborate with cross-functional stakeholders to translate complex business requirements into actionable analytical solutions. Committed to maintaining data compliance and improving business processes within fast-paced professional services environments.
Detail-oriented Operations Associate Analyst with a strong background in data science and quantitative analysis. Experienced in managing large datasets, building automated data pipelines, and creating interactive dashboards to track operational metrics and Key Performance Indicators (KPIs). Proven ability to collaborate with cross-functional stakeholders to translate complex business requirements into actionable analytical solutions. Committed to maintaining data compliance and improving business processes within fast-paced professional services environments.
Built a machine learning model using XGBoost to predict health insurance costs based on demographic and lifestyle factors. Achieved 99.3% model R2 Score, improving pricing and risk assessment. Engineered a data pipeline that improved computational efficiency by 40%. Tools: Python, SQL, Scikit-learn, XGBoost
Designed a CNN-based image classification model using TensorFlow and Keras to detect rice plant diseases. Trained the model on 5,000+ images, achieving 95% test accuracy. Implemented data augmentation techniques to improve model robustness. Deployed the solution using Flask and Docker for real-time predictions.