Rahul Boyidapu

Cleaned and preprocessed data to ensure its quality and suitability for machine learning models. Engineered features to enhance model performance and accuracy. Selected, trained, and evaluated machine learning models, optimizing hyperparameters for improved performance. Deployed and maintained models in production environments. Monitored model performance and initiated retraining when necessary to ensure ongoing accuracy.

Wrote and optimized complex SQL queries to extract, manipulate, and analyze data from databases efficiently. Designed data models and schemas that align with client’s business processes and reporting needs. Identified and resolved performance bottlenecks in SQL queries and database operations to improve system responsiveness.

Deployed a recommendation engine in production, increasing the average order size by 7% through conditional menu item recommendations based on past order history. Implemented time series forecasting techniques (ARIMA and Prophet) to predict surges in customer orders, resulting in appropriate staffing and a 10-minute reduction in average customer wait time. Developed a customer feedback sentiment analysis model using NLP techniques to categorize feedback into positive, negative, or neutral sentiments. Analyzed customer feedback data using Tableau and Power BI to identify trends and improve the restaurant’s menu, service, and overall experience. Collaborated with the marketing team on a personalized marketing campaign, resulting in a 15% increase in customer retention.

Designed and implemented complex database architectures to optimize storage, retrieval, and performance for a high-volume e-commerce platform, resulting in a 20% reduction in response time and improved customer experience. Developed and maintained robust data migration processes, ensuring seamless transition of data from legacy systems to new database platforms, minimizing downtime and data loss. Led the design and implementation of a disaster recovery plan, including regular database backups and automated failover mechanisms, reducing data loss risk by 90% and minimizing system downtime during critical events. Collaborated with cross-functional teams to identify and resolve database performance issues, utilizing advanced troubleshooting and optimization techniques to enhance system reliability and reduce query response time by 30%.

Visakhapatnam

Visakhapatnam

Visakhapatnam