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.
I’m an early-career analyst who likes finding the story inside data. My background blends data science, economics, and computer science, which helps me approach problems from both a technical and business perspective.
My best work happens when I can take a vague business problem, clean and analyze the data behind it, and turn the results into something clear, actionable, and easy for others to use.
A technical stack centered on analytics, reporting, and practical modeling, presented in a way that keeps the section easy to scan.
Data Analyst, Business Analyst, Product Analytics, and Entry-Level Data Scientist roles.
A mix of business analytics, operational reporting, and technical support experience that reflects both analytical depth and strong communication.
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.
These are the strongest portfolio pieces to feature first for entry-level analytics and data science roles. Each card links directly to the repository.
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.
Academic foundation with a practical focus on analytics, data science, and communication.
B.S. in Data Science and Economics, Minor in Computer Science · 2025
Dean’s List