Sai Rakshith Potluri

Data Scientist

As a Data Scientist on ExtraHop's research team, I build and productionize machine-learning systems that power enterprise-scale threat detection and network intelligence. My work spans applied ML research, GenAI tools, and unsupervised pattern mining—advancing ExtraHop's ability to surface real-time malicious activity across complex environments.

I collaborate with security researchers, detection engineers, and platform teams to ensure each model is performant, explainable, and actionable: helping SOCs respond faster and with higher confidence.

• Productionized a streaming SQL Injection detector using Naive Bayes and Firehose telemetry for drift monitoring, containerized and deployed via ExtraHop's async stack; reduced false positives by 53% while maintaining sub-100 ms latency.
• Developed a multi-agent RAG system (LangGraph + GPT-4o + ChromaDB + BGE embeddings) that parsed 2K+ merge requests to surface tribal knowledge and internal conventions—enabled context-aware code reviews and accelerated development.

Data Science Intern

• Designed PySpark pipelines, integrating fragmented retail data into comprehensive promotional datasets.
• Predicted post-promo demand drops using regression models, driving $485K in inventory optimization for a flagship client.

Machine Learning Engineer

Led AI initiatives for one of the world's largest telecom networks to solve real-time customer quality and performance issues.

• Optimized ETL pipelines using PySpark & Apache Airflow, reducing batch job runtime from 13h to 3.3h across 400M+ users.
• Trained an end-to-end Digital Twin for regional internet performance, leveraging LightGBM/XGBoost with advanced feature engineering and tuning—boosted AUC from 0.61 to 0.82 for accurate download speed prediction.
• Used SHAP explainability to drive root cause analysis in 4G/5G failures—powering proactive service interventions.
• Integrated root cause codes into network optimization and marketing use cases