Sai Srinidhi Karanam

•Developed an automated data labeling pipeline using Snorkel and keyword-based functions, increasing labeling efficiency and improving model accuracy by 5%. •Analyzed and identified fraudulent emails and sensitive data, leading to the improvement in threat detection rates. •Validated machine learning detections across millions of emails, ensuring the dataset is accurate for the models. •Updated datasets and models weekly, reducing the false positives and maintaining high-quality performance. •Collaborated with cross-functional teams (threat research, customer success) to proactively mitigate security threats and reduce the response time.

•Developed an ETL system to integrate data from multiple sources, ensuring seamless data ingestion and transformation. •Developed real-time data pipelines leveraging Amazon Kinesis, improving data streaming. •Utilized AWS services (Glue, Athena, EC2, Redshift, DynamoDB, S3) to build scalable and efficient data processing solutions. •Optimized data processing on AWS by leveraging EC2, Athena, Glue, Lambda, and S3, reducing latency and improving cloud performance.

•Implemented LabVIEW-based graphical programming for real-time sensor data analytics •Conducted exploratory data analysis on large-scale industrial datasets, identifying key patterns that improved predictive maintenance models