Prince Patel

Data Scientist with 1+ years of experience leveraging Python, SQL, machine learning and deep learning to build data-driven solutions and scalable models. Proven expertise in predictive modeling, feature engineering and classification modeling, with hands-on experience in price optimization and large-scale predictive analytics projects. Demonstrated ability to translate complex data into actionable insights, improving conversion rates and business performance.

Skilled in model development, data preprocessing, and collaborating with cross-functional teams to drive strategic decision-making.

Data Scientist at TransOrg Analytics (2025-06 – Present)

• Designed and executed an end-to-end machine learning pipeline on multi-flight datasets (40M+ records, 50+ features) including data wrangling, EDA, preprocessing and feature engineering.
• Evaluated multiple classification models and selected an XGBoost classifier to predict paid seat selection probability across price points with strong ROC-AUC performance.
• Built a revenue optimization module by modeling revenue curves and identifying optimal price points to maximize ancillary revenue per passenger.
• Achieved a measurable 0.4% uplift in paid seat selection rate, directly increasing ancillary revenue and revenue per passenger (pax).
• Processed and analyzed large-scale lead datasets (1.6M+ records, 200+ features) across multiple source groups; performed data cleaning, EDA, data wrangling and feature engineering to build robust data pipelines.
• Developed three XGBoost-based machine learning models for CP, Walk-in and Direct channels to predict lead conversion likelihood using cross-validation and hyperparameter tuning.
• Evaluated model performance using sensitivity (recall), specificity and ROC-AUC to ensure balanced detection across high-value lead segments.
• Designed a dynamic probability-based scoring and classification framework to identify high-intent leads, resulting in a 2% increase in high-value lead conversions and improved overall conversion efficiency.

Master of Computer Application (Hons.) in Data Science – Lovely Professional University (2023 – 2025) cgpa: 8.8

Bachelor of Computer Application – Parul University (2020 – 2023) cgpa: 9.11