Data Science with less than a year in ETL, Web Scraping & AI/ML
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Results-driven Data Engineer and Data Scientist with hands-on experience building end-to-end ETL pipelines, web scraping systems, and AI-powered applications. Proficient in Python, SQL, MongoDB, Azure Cosmos DB, GraphQL APIs, and Machine Learning. Proven ability to transform raw, unstructured data into production-ready datasets and actionable insights. Seeking to leverage deep technical skills and project ownership in a high-impact data engineering or data science role.
Dr. D.Y. Patil Agriculture and Technical University, Talsande
B.Tech · Artificial Intelligence & Machine Learning
N/A – June 30, 2026
Optimum Data Analytics
Machine Learning Engineer Trainee
August 1, 2025 – Present
Pune, Maharashtra, India
Nail Disease Detection System
June 24, 2026 – Present
Developed a deep learning image classification model (VGG16 transfer learning) to detect 5+ nail disease categories, achieving 91% validation accuracy. Built a real-time image capture and inference pipeline for practical clinical usability, cutting manual diagnosis time significantly.
AI Patient Triage System
June 24, 2026 – Present
Designed a healthcare chatbot combining predictive ML models with clinical rule-based logic to classify patient urgency in real-time via an interactive Streamlit UI.
Online Fraud Detection System
June 24, 2026 – Present
Built a classification and anomaly detection ML pipeline on financial transaction data, achieving 94%+ fraud detection accuracy through iterative feature engineering and model tuning. Processed and transformed 100K+ records; applied SMOTE for class imbalance, improving recall on fraudulent transactions by 18%.
RAG-based AI Browser Assistant
June 24, 2026 – Present
Built a Chrome extension leveraging Retrieval-Augmented Generation (RAG) to answer user questions from live webpage context, demonstrating full-stack LLM integration.
Cultural Fit Analysis
The candidate's academic projects cover diverse applications of AI/ML, from healthcare to finance and general utility, suggesting a broad interest in the field. The internship role as a Machine Learning Engineer Trainee aligns well with a Data Science career path, indicating a clear career focus. The breadth of technical skills listed also points to an adaptable and continuously learning individual, which is a positive cultural fit for dynamic tech environments. However, the lack of non-academic or team-based projects outside of the internship makes it difficult to fully assess collaboration and broader cultural alignment.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through project work, particularly in addressing challenges like class imbalance (SMOTE) and real-time inference. The internship experience highlights an ability to work on scalable solutions and automate processes, indicating a good operational fit. The academic projects show initiative and a drive to apply theoretical knowledge to practical scenarios.