IATA x IE Datathon

7th edition of the IEU Datathon, in collaboration with IATA and Infosys. Built a predictive model assessing the environmental and economic impacts of SAF adoption in the aviation industry.

Highlights

• Selected as a Top 5 Finalist (out of 38 teams) for demonstrating a system that improves capital efficiency by 95% (reducing the marginal cost of abatement from $633/t to $29/t).
• Architected a proprietary economic framework utilizing a Government-Backed SAF Levy to resolve physical supply constraints in aviation decarbonization, validated by strategic consultations with Qatar Airways leadership.
• Developed a predictive model proving that a SAF Levy integrated with Global Book & Claim eliminates the 15% logistics penalty inherent in current EU mandates.

Tech Stack

• Python (Pandas, NumPy, Matplotlib) - Analysis, Modeling, Viz

Notes

• Really loved diving into a problem in an area of personal interest.
• Refined my ability to research effectively and decide which datasets were of value.
• Grateful to have personally consulted the Qatar Airways Fuel Optimization team, they enormously helped in contextualizing the problem and guiding the direction of our solution.
• Would revisit with a more interactive visualization if I had more time.