ANIRUDDHA PATIL

Data Science graduate – Fresh out of university (April 2026) with experience in end-to-end Python development, machine learning, and NLP-powered applications. Built and deployed GramSetu — a production-grade multilingual platform targeting India's 800M+ rural population — demonstrating the ability to translate complex technical requirements into scalable, high-impact solutions. Proficient across the full data lifecycle: ingestion, wrangling, exploratory analysis, modeling, and visualization. Adept at leveraging AI tools to accelerate delivery. Seeking an entry-level Data Analyst / Junior Data Scientist / Python Developer role at a global MNC where I can drive data-driven decision-making from Day 1

• Second Highest Score in Computer Science — HSC Board Examination
• Shipped GramSetu before graduation — a production-deployed, AI-powered NLP app with real-world users; not a classroom project
• Operates as an AI-native developer: integrates GPT-4, Claude, and GitHub Copilot into daily workflow — consistently compressing build cycles by 30–40%
• Hands-on with Hugging Face Transformers and OpenAI API — applied in academic NLP work; actively exploring LangChain and RAG architectures
• Python for Data Science — completed with distinction-level project implementation
• Maintained a documented, commit-tracked GitHub portfolio — all projects reproducible and open-source

• GramSetu — AI-Powered Multilingual Government Schemes Platform, 12/25 – 03/26, Python, Flask, NLTK, TF-IDF, gTTS, MySQL, HTML/CSS, Architected and deployed a full-stack web application simplifying 50+ government welfare schemes for rural Maharashtra, serving a target population of 800M+ underserved citizens. Engineered an NLP pipeline using TF-IDF and rule-based text simplification to transform dense policy documents into plain-language summaries in Marathi, Hindi, and English. Integrated a text-to-speech (TTS) voice interface enabling illiterate and semi-literate users to access scheme details in their native language — expanding accessibility to previously unreachable demographics. Designed a mobile-first, low-bandwidth responsive UI optimized for 2G/3G connectivity, ensuring inclusion across India's digital divide. Curated and cross-validated a dataset of 50+ schemes directly from official government portals, maintaining 100% data accuracy and regulatory compliance. Demonstrated end-to-end project ownership: requirements → data acquisition → ML pipeline → UI → deployment — mirroring MNC engineering delivery workflows. Accelerated development using GitHub Copilot and GPT-4 for code generation, debugging, and architecture decisions — cutting estimated build time by ~35%.
• Student Performance Analytics Dashboard (Academic Project), 01/26, Python, Pandas, Matplotlib, Seaborn, Streamlit, Statistical Analysis, Performed comprehensive EDA on academic records of 500+ students to uncover performance trends across departments, attendance bands, and assessment types. Built and deployed an interactive Streamlit dashboard with dynamic cohort filtering, enabling stakeholders to explore KPIs in real time without writing code. Applied hypothesis testing and correlation analysis to quantify the impact of attendance and study hours on outcomes, producing insights that informed academic intervention strategies. Delivered findings as 8 publication-ready visualizations, translating raw data into decision-ready insights.