Senior Data Scientist II - Personalization

Careem is building the Everything App for the greater Middle East — making it easy to move around, order food and groceries, manage payments, and more. Our purpose is simple: to simplify and improve people’s lives and build an awesome organisation that inspires.

Since 2012, Careem has enabled earnings for over 2.5 million Captains, simplified the lives of more than 70 million customers, and built a platform where the region’s best talent and entrepreneurs thrive. We operate in 70+ cities across 10 countries, from Morocco to Pakistan.

We’re now entering our next chapter — one powered by AI. We’re looking for AI talent: curious problem-solvers who know how to apply AI to build tools, automate workflows, and create real impact. Whether it’s streamlining operations, enhancing customer experience, or reimagining internal systems — we want people who can make Careem work smarter and move faster.

About The Team:

The Personalization team sits within Careem's Data Science organization and owns the AI systems that decide what every user sees, in what order, and why across Food, Quik, and Shops. Our mission is to build the hyper-personalization layer for the Careem app: real-time, cross-vertical recommendation and ranking systems that learn from a user's behavior in one vertical and apply that understanding everywhere else they engage with Careem. As one of the senior technical leads on this team, you'll help define how Careem thinks about personalization at a regional scale working alongside the region's top data science talent, and pushing the state of the art using graph-based retrieval, transformer architectures, and real-time learning.

What You'll Do:

• Own hyper-personalization use cases across Food, Quik, and Shops designing systems that learn a user's intent and preferences in real time and transfer that signal across verticals, so a user's behavior on one product makes every other product smarter.

• Be a technical lead on Careem's exploration of graph-based retrieval methods for recommendations including evaluating and building knowledge graph pipelines that power candidate generation and ranking at scale.

• Design and evaluate transformer-based architectures (XFY) for sequential and contextual recommendation moving Careem's ranking and retrieval stack beyond classical ML toward deep, attention-based models.