Nithish Kumar Amjolu

About Nithish Kumar Amjolu

Data Scientist specializing in Generative AI and large-scale machine learning systems, with 4+ years of experience building intelligent, data-driven solutions. My expertise lies in designing and deploying LLM-powered applications, including GPT-based tools, LangChain workflows, and Retrieval-Augmented Generation (RAG) systems integrated with vector databases. In addition to Generative AI, I have strong experience in time-series forecasting, anomaly detection, and recommendation systems, leveraging frameworks like PyTorch, TensorFlow, and Scikit-learn. I am also proficient in building scalable data pipelines and ML systems using Apache Spark, Airflow, and cloud platforms such as AWS, GCP, and Azure. I am passionate about pushing the boundaries of AI to create systems that are not only accurate but also interpretable, scalable, and impactful for real-world applications.

Experience

1. Data Scientist

   Walmart

   Jun 2025 — Present · Reston, VA, US

   Enhanced delivery ETA prediction accuracy by 20% using ARIMA, Prophet, and XGBoost on 1.4B+ historical shipment, traffic, and warehouse logistics records to strengthen last-mile delivery predictions across Walmart fulfillment and distribution networks.Mitigated late delivery incidents by 17% by designing dynamic route optimization models using reinforcement learning and graph-based optimization, enabling real-time route adjustments for 40K+ daily delivery vehicles across 160+ fulfillment and distribution centers.Accelerated operational decision-making by 35% by deploying LLM-powered logistics intelligence tools using GPT-4 and LangChain, allowing supply chain analysts to query millions of shipment logs and route events through natural-language interfaces for faster diagnostics.Streamlined delivery issue investigation by 30% by implementing Retrieval-Augmented Generation (RAG) pipelines with vector embeddings stored in Pinecone and FAISS, enabling contextual retrieval across 73K+ historical delivery logs, playbooks, and route exception records. Detected supply chain anomalies 28% more efficiently by developing agent-based monitoring workflows with LangChain agents and real-time data streams, enabling autonomous analysis of delivery disruptions, warehouse delays, and routing constraints across distributed logistics systems.

Education

• George Mason University

  Master's degree, Data Analytics Engineering

  2023 — 2025