Sreesaibandhavi M.
Junior Data Scientist
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M. Sreesaibandhavi is a dedicated Data Scientist and Machine Learning Engineer based in Bangalore, with a robust background in implementing end-to-end machine learning pipelines and innovative Generative AI solutions. With over five production-ready ML projects under her belt, including a customer churn prediction system and AI-powered speech therapy application, she brings a wealth of practical experience. During her internship at AI Variant, Sreesaibandhavi excelled in deploying a RAG-based news summarisation app and enhancing chatbot accuracy via targeted prompt engineering. Her expertise is backed by certifications in Data Science and Generative AI, including skills in Python, Scikit-learn, and LangChain. She holds a Bachelor’s degree from Mohan Babu University and is currently pursuing a Master’s in Computer Applications at Andhra University. Her work is also recognized through an IEEE conference publication, marking her contributions to AI research and development.
Experience
Generative AI Engineer (Intern)
AI Variant · 2025 — 2026Designed and launched a RAG-based news summarisation application utilizing LangChain, embeddings, and vector databases while focusing on prompt engineering to enhance contextual accuracy. Developed LLM pipelines that integrate embeddings, vector stores, and document retrieval for conversational AI and knowledge assistant functionalities. Enhanced chatbot precision by approximately 35% through iterative context optimisation and prompt refinement. Investigated real-time deployment methodologies for Generative AI applications utilizing Streamlit and cloud infrastructures.Data Science Engineer (Intern)
AI Variant · 2025 — 2026Developed a Telecom Customer Churn Prediction system employing Logistic Regression, Random Forest, and XGBoost, with hyperparameter tuning contributing to over 90% accuracy; subsequently deployed as a live Streamlit web application for real-time inference. Created an automated Resume Classification system that utilizes TF-IDF and supervised ML, resulting in a 40% reduction in manual screening effort and enhanced shortlisting precision. Engineered a Book Recommendation engine that combines collaborative filtering and content-based approaches using cosine similarity to provide personalized user recommendations. Conducted comprehensive ML lifecycle tasks encompassing data preprocessing, feature engineering, exploratory data analysis, model evaluation, and visualization for structured datasets. Implemented supervised learning techniques including Decision Trees, SVM, and Gradient Boosting, applying cross-validation and optimization strategies to enhance model performance.
Skills & Expertise
Education
- Master of Computer ApplicationsAndhra University · 2025 — Present
- Bachelor of Computer ApplicationsMohan Babu University · 2022 — 2025