Pavan B Email & Phone Number

New York, NY, US

Plainsboro, New Jersey, United States

• Designed and implemented data pipelines using Azure and Hadoop to process large datasets, improving data processing efficiency by 35%.
• Automated ETL workflows using AWS Glue and Apache Airflow, reducing manual intervention by 40% and streamlining data ingestion and transformation from multiple sources.
• Developed and maintained scalable storage solutions with AWS S3 and Azure Blob Storage, reducing storage costs by 20% while ensuring high availability and data security.
• Optimized distributed data processing systems using Hadoop MapReduce and Apache Spark, enhancing real-time processing and reducing latency for large-scale data applications.
• Deployed machine learning models using AWS SageMaker and Azure Machine Learning, improving predictive accuracy and speeding up model deployment by 25%.
• Implemented real-time streaming data pipelines using Apache Kafka and Spark Streaming, ensuring low-latency data delivery across teams for real-time analytics.

Vellore, Tamil Nadu, India

• Collaborated on designing Standard Operating Procedures (SOPs) for Exploratory Data Analysis (EDA), focusing on quantile and power transformations to reduce data noise by 20%.
• Developed machine learning models using Python, TensorFlow, and Keras for healthcare applications, achieving 90% accuracy for Parkinson’s disease classification and 99% accuracy for diabetes prediction.
• Implemented scalable ETL workflows on Azure Data Factory, automating data ingestion and transformation, reducing manual intervention by 40%.
• Designed distributed data processing pipelines using Azure Databricks and Apache Hadoop, improving data processing efficiency by 35%.
• Built and managed real-time data streaming systems using Apache Kafka and Spark Streaming, ensuring low-latency processing of anomaly detection tasks.
• Optimized performance and scalability of machine learning models on Azure Virtual Machines, reducing system downtime by 30% and improving data reliability.

Hyderabad, Telangana, India

• Developed and supervised data collection using Python and SQL, gathering over 500 biker feedback entries for operational improvements. Applied NLP techniques with SpaCy and Pandas for content summarization.
• Deployed an NLP sentiment analysis system on Azure, using Keras for model training, to assess employee sentiment and reduce attrition risks.
• Used K-Means clustering with Scikit-learn to group similar feedback, enabling targeted retention strategies. This approach resulted in a 10% increase in biker retention through improved, data-driven employee engagement.

Master Of Science - Ms, Data Science, 3.6

Bachelor Of Technology - Btech, Electronics And Communications Engineering, 7.71

Activities and Societies: Volleyball, Cricket.- Undergraduate studies helped me develope intrests in Computing, finance and Data science..