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Data Science with 1+ years in Machine Learning, Deep Learning, NLP, and Generative AI
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Data Scientist with hands-on experience in Machine Learning, Deep Learning, NLP, and Generative AI, along with strong skills in data extraction, cleaning, EDA, and dashboarding. Proficient in Python, Pandas, SQL, Power BI, scikit-learn, TensorFlow, Selenium, LangChain, and CrewAI, with a proven ability to transform raw data into actionable business insights.
Guru Nanak Dev University
Master of Computer Applications · Computer Applications
August 1, 2021 – June 30, 2024
Guru Nanak Dev University, College
Bachelor of Science
August 1, 2018 – June 30, 2021
Elevance Skills
Data Scientist Intern
November 1, 2025 – May 1, 2026
India
InnoByte Services
Full Stack Developer Intern
September 1, 2025 – September 1, 2025
India
NullClass EdTech Pvt. Ltd.
Data Scientist Intern
October 1, 2024 – June 1, 2025
India
Multi-hop query POC for PDF documents
November 1, 2025 – May 1, 2026
Engineered a multi-hop query POC for PDF documents leveraging a multi-agent framework, specifically CrewAI. Implemented an advanced Retrieval-Augmented Generation (RAG) pipeline for PDF document ingestion, converting text chunks into vector embeddings and persisting them within a FAISS vector store to facilitate efficient semantic search and information retrieval. This methodology enhances the precision and speed of information retrieval across extensive unstructured PDF datasets, yielding pertinent insights.
Automated Web-Scraping Pipeline for Product Data
November 1, 2025 – May 1, 2026
Developed an automated web-scraping pipeline using Python and Selenium to extract large-scale product data from Snapdeal, including price, discount, ratings, reviews, brand, and seller details across multiple categories. Performed comprehensive data cleaning and preprocessing in Python (Pandas) by removing duplicates, handling missing values, converting discount percentages to numeric format, and standardizing categorical fields for analysis. Conducted exploratory data analysis (EDA) to study relationships between price, discount, and ratings, and generated actionable insights such as "high discounts do not necessarily improve ratings." Built interactive dashboards in Power BI, visualizing metrics like average price vs rating, discount distribution, and category-wise performance using scatter plots, heatmaps, and combo charts to support data-driven decision making.
RESTful API for Blog Posts
September 1, 2025 – September 1, 2025
Developed a RESTful API using Flask to manage blog posts and comments, implementing full CRUD operations with validation and error handling. Designed and integrated SQLite database schema with relational mapping for users, posts, and comments. Implemented JWT-based authentication and role-based access control, securing endpoints for authorized actions only. Performed API testing and validation in Postman, ensuring reliability and correctness of request/response flows.
Automated Vehicle Number Plate Recognition (ANPR) System
October 1, 2024 – June 1, 2025
Architected and deployed an advanced automated vehicle number plate recognition (ANPR) system utilizing sophisticated camera technology and image recognition algorithms to eliminate manual toll payment processes. Optimized the toll collection workflow through the automation of vehicle detection and identification, demonstrably reducing traffic congestion and minimizing user wait times at toll plazas, thereby enhancing overall efficiency and user satisfaction. Implemented real-time vehicle identification and data logging functionalities, ensuring seamless and instantaneous toll transactions, contributing to improved operational throughput and data integrity. Streamlined operational expenditures by minimizing dependence on manual personnel and developed a scalable architectural solution suitable for widespread deployment across numerous toll roads and geographical areas, ensuring extensibility and future adaptability.
Real-time Student Segmentation Analysis
October 1, 2024 – June 1, 2025
Conducted real-time student segmentation analysis to identify key student cohorts based on enrollment patterns and course engagement in courses, enabling improved targeted learning. Performed preprocessing and cleansing of student enrollment datasets by handling missing values, duplicates, and invalid or cancelled application records to ensure data accuracy and consistency. Implemented K-Means clustering algorithms and applied the Elbow Method along with Silhouette Score analysis, followed by 3D visualization of student segments based on course application behavior. Applied logarithmic scaling transformations to reduce data skewness and enhance clustering performance, resulting in interpretable student segmentation insights for course preference analysis.
Machine Learning Specialization
Unknown
June 1, 2026 – Present
Deep Learning Specialization
Unknown
June 1, 2026 – Present
State-Merit in 10th standard for securing 96 percent.
Unknown
June 1, 2026 – Present
Practice coding questions in different coding platforms like GeeksforGeeks, Leetcode.
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project portfolio shows a breadth of interests within data science, including NLP, Generative AI, web scraping, and traditional ML. This diversity suggests adaptability and a willingness to explore different problem domains. The target role of 'Data Science' aligns well with the candidate's stated skills and project experience. However, all experience is at an intern level, which might indicate a need for more structured mentorship in a full-time senior role.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on diverse tasks, from data extraction and cleaning to model implementation and dashboarding. The focus on optimizing workflows and generating actionable insights suggests a results-oriented approach. However, the experience is primarily at an intern level, which may require more guidance in a senior operational role.