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Created predictive models for asset price forecasting using XGBoost and ARIMA, enhancing forecast predictions of market trends by 25% and supporting investment strategies with Apache Spark. Developed generative AI models (e.g., GANs) to simulate market scenarios, heightening stakeholder insights and increasing market prediction accuracy by 30% using Google BigQuery and AWS Redshift. Optimized and

Implemented a personalized product recommendation system using collaborative filtering, matrix factorization, and content-based filtering algorithms, improving product relevance and increasing conversion rates by 15%. Developed time series forecasting models using ARIMA and Prophet to improve inventory management and reduce stockouts and overstock by 12%. Created computer vision models to automate

Created and implemented predictive models to forecast insurance claim approvals, detect fraud, and improve risk assessment utilizing Python, SQL, Scikit-learn, and XGBoost. Leveraged AWS cloud services (e.g., S3, Lambda, Redshift) and Google Cloud Platform (GCP) to store, process, and analyze healthcare and insurance data at scale, reducing processing time by 30%. Developed and managed real-time d

Developed ETL pipelines to connect data from internal insurance systems, external third-party vendors, and customer interactions, increasing data accuracy and reducing overall processing time by 30%. Designed and optimized relational databases such as MySQL and PostgreSQL for high-performance data storage and retrieval, improving system performance by 25%. Created and managed Data Lakes on AWS S3

Completed a Bachelor of Science degree focusing on data science principles. Gained expertise in data analysis, statistical modeling, machine learning algorithms, and programming for data-driven solutions.