I’m Ahan Nair, a data science and economics graduate focused on analytics, automation, dashboards, and applied machine learning. I enjoy building practical projects that connect technical work to real business questions.
Early-career analyst with a background in data science, economics, and computer science.
Focused on analysis, modeling, dashboards, and clear communication of results.
Core tools used across analytics, visualization, reporting, and modeling projects.
Data Analyst, Business Analyst, Product Analytics, and Entry-Level Data Scientist roles.
Experience across analytics, reporting, automation, and technical support.
Support analytics across Exec Socks and F45 by analyzing customer, revenue, and membership data to identify growth, retention, and spending trends. Build dashboards, prepare ETL-style datasets, and design automation workflows that reduce manual reporting and help leadership make clearer decisions.
Resolved 1,000+ support interactions across in-person, phone, chat, and ticketing channels while documenting issue patterns and improving troubleshooting workflows. The role strengthened my ability to communicate technical concepts clearly and solve problems in fast-moving environments.
Selected projects in healthcare, business, and sports analytics.
A football analytics case study built around a polished Jupyter notebook using StatsBomb-based action data and xT-style action valuation. It adds variety to the portfolio by showing possession-value analysis, team style insights, and player-style clustering in a more research-driven format.
Notebook-based analysis using synthetic Medicare claims data to identify major cost drivers and simulate real-world healthcare analytics workflows.
A machine learning pipeline focused on predicting patient length of stay, showing feature preparation, modeling, and evaluation for a practical healthcare use case.
Analyzes drivers of hospital readmission for diabetes-related cases, combining exploratory analysis with predictive thinking around patient outcomes.
Uses CFPB consumer complaint data to surface risk signals and process breakdowns tied to revenue leakage themes like fraud, billing disputes, refunds, and response performance.
An interactive explorer project that presents a more application-style way to inspect data, which adds variety beyond notebooks and shows you can build usable interfaces around analysis.
Education background in data science, economics, and computer science.
B.S. in Data Science and Economics, Minor in Computer Science · 2025
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