Anastassiya Rezayeva

Machine Learning Engineer

Built automated airplane ticket-processing pipelines using Python, reducing manual booking operations by ~35% and improving processing speed across Amadeus/Sabre systems. Analyzed high-volume travel transaction data (~10K+ records/day) to identify flight booking patterns and optimize routing logic, improving efficiency of ticket handling workflows. Developed rule-based recommendation logic for fare optimization, increasing cost-efficiency of ticket selection by ~12%. Designed data extraction and transformation workflows from GDS terminals into structured datasets for downstream analytics (Pandas, SQL). Implemented anomaly detection heuristics to flag booking inconsistencies, reducing ticketing errors by ~18%.

• Reduced manual booking operations by ~35%
• Increased cost-efficiency of ticket selection by ~12%
• Reduced ticketing errors by ~18%

Lecturer & Teaching Assistant - Machine Learning Algorithms

Designed and delivered ML Algorithms curriculum with hands-on Python labs (classification, clustering, regularization, cross-validation, evaluation), driving a class average of 86% with 0 failures. Mentored student ML assignments and project reviews, achieving 95% on-time completion (individual) and 90% (group) by enforcing milestone-based delivery and rubric-driven feedback loops. Implemented diverse teaching methodologies and utilized various learning facilities, resulting in 18% of students achieving straight A grades. Received a 98% satisfaction rate from student feedback surveys on communication and presentation effectiveness.

• Class average of 86% with 0 failures
• 95% on-time completion (individual), 90% (group)
• 18% of students achieved straight A grades
• 98% satisfaction rate from student feedback surveys

AI / ML Engineer Intern

Designed and deployed NLP predictive analytics features into a consumer application, decreasing churn by 17% by improving personalization and in-app discovery (ML prototypes in Python). Increased prediction accuracy by 21% and reduced processing time by 30% by iterating on feature engineering + model selection and validating improvements against a consistent offline benchmark. Implemented AI-driven modules (speech recognition, image classification, recommendation) in Swift using clean OOP design, enabling reliable inference behavior and maintainable integration points.

• Decreased churn by 17%
• Increased prediction accuracy by 21%
• Reduced processing time by 30%

Software Engineering Intern

Implemented ML-powered iOS features in Swift (RxSwift, SnapKit, VIPER), integrating on-device inference and optimizing data pipelines to improve database efficiency by 30% and key user-experience metrics by 20%. Profiled performance hotspots, reduced memory usage, and implemented safe multithreading for UI-critical paths, increasing app responsiveness by 40%. Raised code quality and delivery reliability by adding unit/UI tests, enabling CI checks, and participating in code reviews to streamline ML feature rollouts.

• Improved database efficiency by 30%
• Improved key user-experience metrics by 20%
• Increased app responsiveness by 40%