• Built a Request Specific Price (RSP) model to quote individual pricing for airline passengers• RSP is a Poisson non-parametric deep neural network demand response model with feature-dependent price sensitivity which evaluates Willingness to Pay (WTP)• Reduced data sparsity by clustering mechanism based on Kmeans- Silhouette scores and cross-validated using Principal component analysis (PCA)

• Successfully led a regional cross-functional team (11 Analysts) to achieve ~1.1Bln target for three consecutive years• Conceptualized, negotiated, and implemented commercial strategic plans & customer propositions to drive multiple stakeholders (Sales, Dealing, and Marketing) to achieve seat and ancillary revenue targets• Led partnership strategies for the Revenue Management department with equity Partner Jetairways (9W), Air Berlin (AB), and Virgin Australia (VA)• Led major organizational transition and working groups focused on roles, processes, and analytics with 50+ employees directly affected. Established department-level work plans, user level workflows and managed related training & change to introduce an effective management framework successfully• Designed Key Performance Indicators (KPIs) for the department (~ 15 mid-level managers, 50 Analysts)• Led user implementation of various analytical tools such as Dynamic Pricing, Machine learning models for Ancillaries Pricing etc

• Attained the highest accuracy in time series demand forecasting, resulting in incremental revenue of ~1.5 M USD for Australia and Far East point of sales• Led customer segmentation project, and built a Random Forest model with an accuracy of over 90%• Increased customer retention by 10% and B2B revenue by 5% by introducing a channel-based pricing strategy based on customer insight analysis focusing on optimizing inventory • Evaluated promotional activity using A-B testing, thereby creating marketing plans with the spend of ~2.5MUSD• Seconded to Air Berlin to upskill a team of 25 Managers/Analysts due to my consistent outperformance

• Built a linear regression model to forecast cancellation rates and no-shows, which reduced seat spoilage in my portfolio by 20%. The model was adopted across the department, which saved revenue spoilage of ~3 MUSD yearly• Implemented a mathematical model to optimize the Booking Velocity at flight level, which analysts adopted to maximize revenue on filtered flights generating ~$350M• Developed a Macroeconomic regression model for Revenue Forecast for Etihad with over 95% accuracy which was used to cross-validate network planning inputs