Ikram Ali

I built a machine learning-powered hotel room ranking and discovery system for KAYAK to improve room matching, search relevance, and room comparison quality. The system helped users find more accurate and affordable room options by categorizing hotel rooms using raw provider data, price signals, and room type information.

I worked on the applied ML and product side, collaborating with product, data, and engineering teams to improve hotel discovery and search quality. The project contributed to a 23% improvement in KAYAK search index ratings, increased user engagement, and generated 7% additional revenue.

I developed review intelligence systems that automatically generated pros, cons, key themes, and topic tags from user reviews across hotels, airlines, and cars. The system helped users quickly understand important review details and improved organic search visibility through concise, keyword-rich content.

I designed and contributed to NLP pipelines that extracted meaningful phrases from review text and converted unstructured review content into searchable, user-friendly summaries. The project improved user engagement and session duration by approximately 35%.

I built an ML-based image tagging system to automatically annotate millions of hotel images using stakeholder-defined categories. The system also included a watermark-detection model to filter out low-quality visuals and improve the quality of hotel image collections.

The image tags improved hotel detail pages, supported SEO, and helped users make better booking decisions using clear visual cues. The project boosted user sessions by 10% and improved conversion rates by 6%.