Haritha Anand

I began my journey in data analytics, working with SQL, dashboards, and business metrics to understand how data drives real decisions. What fascinated me early on wasn’t just reporting numbers, but uncovering why patterns existed and how systems could adapt intelligently. That curiosity led me toward machine learning and AI, where models move beyond explaining the past to shaping user experiences. I recently completed my Master’s in Data Analytics Engineering at Northeastern University in December 2025. My work sits at the intersection of data, machine learning, recommendation systems, and AI-powered applications, and I enjoy building systems end-to-end from raw data and experimentation to user-facing intelligence. I’m seeking full-time roles in AI/ML engineering, Data Engineering, Data Science, and Data Analyst roles. Previously, I worked as an ML Engineer at Kubby, a commerce and discovery platform, where I focused on building the recommendation system and AI chatbot. I worked with large-scale user interaction and product data to design analytics-ready schemas, validate events, and build pipelines for feature engineering and model training. I trained and evaluated ranking-based recommendation models using collaborative filtering and content-based approaches, and contributed to a GPT-4–powered RAG chatbot, focusing on retrieval quality, prompt design, latency monitoring, and deployment with product and engineering teams. Earlier, as a Data Science Intern at AssetPlus Consulting, I worked in the energy and utilities analytics domain, analyzing consumption and loss patterns and building SQL-backed Power BI dashboards for KPI and anomaly tracking. At Flare Innovation, an agri-tech startup, I worked in the sensor and operational analytics domain, building Python-based ETL pipelines on AWS, optimizing SQL queries, and creating Excel and BI reports for operational monitoring. I’ve explored AI through projects including: • Agentic AI Research System – multi-agent reasoning over documents using LangGraph, GPT-4, embeddings, and vector search • ClimaSmart – cloud-based analytics platform automating data ingestion and forecasting workflows • Text-to-Image Generation – diffusion-based generative modeling • Business & Workforce Dashboards – SQL-driven analytics in Tableau and Power BI • Sentiment Analysis – NLP-based text classification and evaluation My core skills include Python, SQL, machine learning, recommendation systems, LLMs, RAG, embeddings, vector search, NLP, dashboards, cloud platforms (AWS/GCP), and data pipeline automation.