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RoveMiles — ML Engineering + Financial Modeling Work
NewYork
Date
May 2025
Machine Learning Intern
Built end-to-end predictive analytics and financial modeling system for RoveMiles.
Build end-to-end predictive analytics and financial modeling system.
Approach:
Developed churn prediction, anomaly detection, KPI tracking, and a comprehensive revenue forecasting model with automated ML pipeline integration.
Approach / Methodology:
Developed churn prediction and retention models using Scikit-Learn to identify at-risk customers.
Implemented anomaly detection on booking and transaction logs to surface irregular patterns in real time.
Built a comprehensive financial forecasting model in Excel covering GMV, blended take rate, revenue curves, partner payouts, and profit margins.
Conducted sensitivity analysis on mile issuance and redemption behavior to evaluate profitability under varying assumptions.
Designed a complete ML pipeline lifecycle — from data ingestion and validation to feature engineering, model training, and monitoring.
Link
Custom
Key Learnings:
Business-focused machine learning
Working with ambiguous real-world data
Building financial models and ML systems together
Tech Stack:
Python, Pandas, Excel, Scikit-Learn, AWS S3, Jupyter
Building an end-to-end predictive analytics and financial modeling system for RoveMiles. Through our approach of developing churn prediction, anomaly detection, KPI tracking, and a comprehensive revenue forecasting model with automated ML pipeline integration, we were able to deliver a financial model, identify high-impact churn features, and build metrics dashboards for KPIs and revenue.
