Data Science with less than a year in AI, Machine Learning & Data Analytics
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Results-driven Data Science Intern with a strong foundation in big data analytics and practical experience in developing AI-driven solutions. Proven ability to engineer scalable data pipelines, perform end-to-end data preparation, and create insightful visualizations. Adept at leveraging machine learning for predictive modeling and optimizing business operations through innovative project implementations.
St. Xavier's College
M.Sc. · Big Data Analytics
August 1, 2024 – June 30, 2026
ITM (SLS) Baroda University
Bachelor of Computer Applications
August 1, 2021 – June 30, 2024
Petpooja
Data Science Intern
December 1, 2025 – Present
Ahmedabad, Gujarat, India
MaMo Technolabs
Data Research Analyst Intern
January 1, 2024 – April 1, 2024
Vadodara, Gujarat, India
Flight Delay Time Series Analysis
June 1, 2026 – Present
Performed exploratory time series analysis on U.S. flight delay data to identify daily trends and delay patterns, applying ADF stationarity testing and ACF/PACF autocorrelation visualisation to detect seasonality. Built and trained an LSTM neural network for forecasting daily flight delays, outperforming traditional statistical methods in predictive accuracy. Leveraged Python (Pandas, Statsmodels, TensorFlow, Matplotlib) for end-to-end preprocessing, model training, and visualisation of results.
View ProjectVoice-Based Ordering System
June 1, 2026 – Present
Engineered a 7-stage multilingual AI pipeline for restaurant food ordering in English, Hindi, and Gujarati using Sarvam AI saaras:v3 ASR + Silero VAD, achieving 0.95+ match confidence with significant noise reduction via parallel async processing. Built a hybrid NLP matching engine (Sentence Transformers + RapidFuzz) with LLaMA 3 70B classification via Groq API, delivering 88-95% order accuracy across exact, phonetic, and cross-language utterances with real-time multilingual correction handling. Deployed full-stack system on a single FastAPI process managing 5 concurrent table sessions, real-time admin dashboard, and inventory validation across 32 menu items with zero state contamination.
View ProjectAutomated Inventory Management System
June 1, 2026 – Present
Built a 10-stage barcode scanning pipeline using YOLOv11 nano (18ms/frame) + OpenCV (CLAHE, RANSAC ±35° correction), achieving 0.90+ detection confidence with two-level decoding (pyzbar + Tesseract OCR fallback) covering 88-92% of barcodes. Implemented business logic validation (SKU check, 5-sec duplicate detection), improving stock record accuracy from 65-75% to 99%+ and reducing update latency from 8+ hours to real-time. Deployed on a single FastAPI process with PostgreSQL atomic transactions, Redis caching (<2ms reads), 4-tier JWT RBAC, and a live React.js dashboard for real-time inventory visibility.
Data Science Foundation
Unknown
June 1, 2026 – Present
R Programming
Unknown
June 1, 2026 – Present
Python for Data Science
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects showcase a proactive and innovative approach to problem-solving, which aligns well with a culture that values initiative and technical depth. The diversity of projects (voice AI, time series, inventory management) indicates adaptability and a broad interest in applying data science to various domains. The current enrollment in an M.Sc. program suggests a commitment to continuous learning and professional development.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through complex project implementations. The ability to work with diverse technologies and integrate multiple components suggests good operational fit for dynamic environments. The descriptions indicate a results-oriented approach, focusing on accuracy and efficiency improvements.