Civic Tech Volunteer | Hack for LA
Business Analytics, ASU • W.P. Carey, MSU Law School • Engineering at MIT
PortfolioI am an analytics professional with experience spanning business analytics, applied data science, and machine learning, focused on using data to drive insight, inform decisions, and solve complex business problems through structured analysis and modeling.
I completed my Master of Science in Business Analytics from the W. P. Carey School of Business at Arizona State University, building a strong foundation in statistics, machine learning, and analytics strategy. This was shaped by my undergraduate training in engineering and complemented by formal legal studies, which together strengthened my quantitative thinking and analytical rigor.
With extensive experience in Business and Data Analytics, Science, Machine Learning, and Marketing Research, I work closely with machine learning models and real-world datasets to derive meaningful insights and drive outcomes. My work involves building and evaluating models using techniques such as regression, classification, clustering, forecasting, and natural language processing, while leveraging Python and SQL to work across structured and unstructured data. I focus on developing solutions that are both technically sound and practically impactful.
My passion lies in data and technology-driven companies, and I am enthusiastic about leveraging my skills to make impactful contributions in this dynamic field. As a Data Professional, I am committed to advancing my expertise and contributing to the tech industry.
Let's connect and explore how data and technology can transform our world!
Connect with me!
Graduate Research Assistant
Data Scientist
Tempe, AZ
ASU Capstone Project
Data Analyst
Phoenix, AZ
Central Analytics Team
Business Analyst, Associate
Vadodara, India
Digital Marketing Team
Associate Business Analyst
Pune, India
DS - AI - ML Team
Artificial Intelligence Intern
Kanpur, India
FP&A Analytics & Forecasting for
Built a dynamic forecasting model integrating contractual and transactional revenue to improve financial planning, risk visibility, and scenario-based decision making.
Data Science for
Live queue and wait-time intelligence to improve campus dining experience and operations.
Operations & Revenue Analytics
Identified revenue leakage, cancellation drivers, and city-level inefficiencies for 
through SQL-based analysis.
ML & Deep Learning
Developed a deep learning–based object recognition system for multi-class image classification.
Machine Learning & Risk Analytics
Automated credit score classification into risk tiers to support scalable financial decision-making.
Marketing Analytics & Customer Segmentation
Identified customer segments and competitive positioning for 
to inform data-driven retail marketing strategy.
