• Spearheaded multiple high-impact projects, leading cross-functional teams to deliver scalable data solutions aligned with business objectives.

• Mentored junior engineers and fostered a collaborative environment to drive innovation and excellence.

• Optimized big data infrastructures using Apache Spark and Scala, enhancing data processing efficiency by 95%.

• Expertise on clearing tech debt and designing optimized large scale systems.

• Designed and deployed AI agents operations infrastructure, integrating LLMOps best practices to streamline the deployment, monitoring, and maintenance of machine learning models. Ensured seamless scalability and reliability of AI-driven applications.

Led a cross-functional data science team at Verizon, driving key analytics initiatives for wireless and wireline services. Spearheaded creation of scalable data platforms and real-time APIs using Apache Spark and AWS. Designed and implemented machine learning algorithms to enhance network performance for over 300 million subscribers.

Implemented production-ready ML solutions using containerized microservices and cloud infrastructures (AWS/GCP), cutting deployment times by 40%.

Responsible for collecting, structuring and analyzing the data from various internal and external data sources, ensure data quality and consistency of data flow.

Design and develop the scalable solution that would enable storytelling visualization and advanced analytics capabilities.

Handling data processing, data modeling, application design, development, testing/implementation, and data visualization/reporting.

Make connections between data derived from different sources and synthesize it into one complete picture.

• Developed a smart web crawler to extract news from Bloomberg.com and performed sentiment analysis with Python.

• Utilized various machine learning models like SVM, Random Forest, and DNN to predict fraudulent activities in an e-commerce dataset.

• Analyzed time-series stock data using Python and R to make informed decisions.