Hi, I'm Ishaan Bhutada!

My journey into analytics did not begin with algorithms; it began with curiosity. I have always been fascinated by how data, when analyzed with intent, can turn everyday operations into measurable impact. That belief continues to shape my approach to solving complex business problems today.

At the Carlson School of Management, where I recently completed my Master’s in Business Analytics, I consulted for multiple organizations through the Carlson Analytics Lab. For Sun Country Airlines, I built a DistilBERT-based NLP pipeline to classify over 240,000 safety reports, reducing manual review time by 74% and providing real-time risk dashboards in Tableau. For a regional agri-nonprofit, I applied Python, R, and SQL to design a geospatial forecasting model that guided a $2 million warehouse expansion and cut logistics costs by 48%. I also used causal inference techniques such as Propensity Score Matching and Difference-in-Differences to measure operational efficiency improvements of 7.4%.

Before graduate school, I worked at the Union Bank of Switzerland, where I engineered SQL-based ETL pipelines, deployed CI/CD automation, and integrated cross-platform trade data to enhance compliance accuracy. These solutions improved audit-readiness by 32% and saved over 40 hours per week in manual effort.

Earlier, at Load and Road Incorporated in Japan, I supported the U.S. launch of a sensor-based smart teapot by developing Power BI dashboards to monitor campaign performance and building a customer churn model in Databricks using XGBoost and SHAP. This model improved retention by 28% and reduced data processing time by 38% through automated Python and SQL workflows.

Across these experiences, I have worked end-to-end across the analytics lifecycle - scoping business problems, engineering data pipelines, building predictive models, and designing executive dashboards that translate data into strategy.

I believe that the true value of analytics lies not in the models themselves but in the stories they tell and the actions they inspire. If you are building data-driven solutions that combine rigor with impact, I would love to connect.

Latest Projects

Smart Admissions Assistant: RAG-Powered Chatbot

The Smart Admissions Assistant is a Retrieval-Augmented Generation (RAG) chatbot system designed to revolutionize how prospective students interact with institutional admissions information. By combining OpenAI's advanced language models with efficient semantic search capabilities, this project creates an intelligent assistant that provides accurate, document-grounded responses to student queries about the admissions process. The implementation achieved an 80% reduction in query resolution time and maintains over 95% response accuracy through systematic document citation, demonstrating the practical effectiveness of RAG architectures in educational technology applications.

Geospatial Credit Risk Modeling: DBSCAN Clustering for Subprime Segmentation

The Geospatial Credit Risk Modeling project applies advanced unsupervised learning techniques to segment subprime and thin-file credit applicants using geospatial clustering. By implementing DBSCAN on customer geolocation data enriched with socioeconomic features, this project identified four distinct risk profiles with a silhouette score of 0.53. Integration with XGBoost predictive modeling achieved an AUC-ROC of 0.74, representing a 9% improvement over baseline models. The implementation reduced portfolio default rates by 18% while increasing approval rates for lower-risk segments by 23%, demonstrating effective balance between risk management and financial inclusion.

Sentiment Sphere: Real-Time Sentiment Analysis

Sentiment Sphere is an innovative real-time sentiment analysis solution designed to capture and interpret emotional expressions from social media platforms instantly. Using advanced natural language processing (NLP) tools, this pipeline effectively addresses challenges posed by informal language, slang, emojis, and sarcasm commonly found on platforms like Twitter, Reddit, and Facebook. This comprehensive solution provides valuable insights into public opinion and sentiment trends, significantly aiding marketing, customer relations, and crisis management efforts.

Real-Time American Sign Language Interpreter: CNN-Based Finger Motion Capture

The Real-Time American Sign Language Interpreter is a computer vision and deep learning system designed to bridge communication barriers for deaf and mute individuals. By implementing a multi-model Convolutional Neural Network architecture trained on custom captured datasets, this project translates ASL fingerspelling gestures into English text in real-time using standard webcam input. The implementation achieved 99.3% classification accuracy across all 26 English alphabets through an innovative hierarchical model approach that addresses visual ambiguity in similar hand signs. The research findings were published in the International Journal for Research in Applied Science & Engineering Technology, contributing to the academic discourse on accessible assistive technologies.