Hey, I'm Pranav Azad,

A Data & Analytics Professional.

I'm a data enthusiast who enjoys building insightful dashboards, automating workflows, and exploring new technologies.

Resume

My professional journey and work experience.

Jan. 2025 - Present

Undergraduate Research Assistant

Carlisle, PA

Engineered an AI system of over 15 models utilizing PyTorch, RNNs, and DNN for analytical modeling of human Rubik’s Cube solving, training on over 350,000 solve permutations to adapt to diverse solving styles and skill levels, achieving over 99% accuracy.
Developed a robust 6-matrix-based cube representation using NumPy and ASTs (Abstract Syntax Trees), facilitating the AI’s data management and simulation of complex cube transformations and state changes for quantitative analysis.
Designed and implemented an automated data collection pipeline for real-world solve data from Smart Cubes, obtaining over 10000 solves.
Integrated timestamps and move sequences directly into the AI training pipeline to enhance model accuracy and real-world applicability for data-driven business decisions, achieving 18% model improvement.

Jan. 2025 - May 2025

Academic Technology Desk Assistant

Carlisle, PA

Provided end-to-end technical support to an average of 20+ daily students, faculty, and staff, resolving diverse technology issues and enhancing user productivity.
Configured and troubleshot specialized tech equipment, including 3D printers, laser cutters, and multimedia tools, ensuring operational readiness for 10+ weekly academic projects and workshops.
Streamlined equipment and room reservation processes, improving resource utilization by 20% and consistently maintaining customer satisfaction rate in a high-volume academic setting.

May 2022 – Jun. 2022

Data Science Intern

(Remote)

Pioneered a machine learning model using Linear Regression in Scikit-learn to detect bank loan fraud from a dataset of over 100,000 entries, collaborating with a team of 3 to achieve 94% accuracy.
Constructed a predictive model for Boston housing prices, analyzing a dataset exceeding 100,000 entries and employing Pandas, NumPy, and Matplotlib for data preprocessing, analysis, and visualization.