Data Scientist specializing in machine learning, time-series forecasting, and computer vision.
Experienced in building end-to-end data-driven systems, with a strong interest in applied research and real-world impact.
LSTM + daily retraining pipeline
Built an LSTM-based time-series forecasting pipeline for stock price prediction using historical market data, feature engineering, and model evaluation to assess real-world performance.
Computer Vision & CNN-based inference system
Developed a real-time computer vision system for face detection and emotion classification using CNNs and OpenCV, optimized for live inference.
AWS Lambda + real-time dashboard
Low-latency anomaly detection pipeline for crypto price and volume anomalies using serverless functions and a live dashboard for alerts.
Retrieval-Augmented Generation chatbot
A RAG-based chatbot supporting document uploads, search, and fine-grained Q&A over user files. Built with FastAPI and vector DBs.
I am a Data Scientist with a strong foundation in machine learning, statistical modeling, and data analysis, focused on building data-driven systems that are both theoretically sound and practically deployable. My work spans time-series forecasting, computer vision, and applied research, with hands-on experience across the full machine learning lifecycle.
I have worked extensively with real-world datasets, designing end-to-end pipelines that involve data preprocessing, feature engineering, model development, evaluation, and deployment. My technical experience includes deep learning models such as LSTMs and CNNs, as well as classical machine learning techniques for structured data and predictive analytics.
In addition to applied industry projects, I have research experience and a published research paper, reflecting my interest in rigorous experimentation and analytical thinking. I am actively seeking roles in data science, data analysis, machine learning, and research, where I can contribute to impactful data-driven decision-making while continuing to grow as a practitioner and researcher.
University of Maryland, College Park
CGPA: 3.86 · Expected May 2026
University of Petroleum and Energy Studies
CGPA: 7.92 · June 2020
IEEE ISMSIT 2022
Developed a GAN model coupled with Improved Evolutionary Computing (IEC) to generate high-quality synthetic images. Achieved a 94% realism rating and reduced model collapse by 20% through improved training strategies.
July 2022 – July 2024
June 2020 – June 2022
I’m open to roles across data science, analytics, engineering, and research. The best way to reach me is via email or LinkedIn.