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: 

• Drive real-time, cross-vertical personalization: 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.
• Advance graph-based retrieval: 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.
• Build next-generation ranking models: 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.
• Pioneer real-time learning: Push toward online/streaming learning systems that adapt to user behavior within a session, not just from batch-trained models refreshed on a daily cadence.
• Build for cross-learning: Identify where personalization signals, models, or infrastructure can be shared across Food, Quik, and Shops rather than rebuilt per vertical reducing duplicate work and compounding the value of every experiment.
• Be part of a 0-to-1 AI transformation for the Careem app from a personalization standpoint shaping how generative AI and LLM-based systems augment retrieval and ranking.
• Build a long-term vision for how Careem rethinks customer acquisition and engagement strategies, grounded in data-driven decision-making.
• Drive exploratory analysis to understand user behavior across verticals, identifying new levers to move metrics and building behavioral models that inform product enhancements.
• Shape and influence the ML models and instrumentation that optimize the product experience, surfacing new areas of opportunity and new product directions.
• Provide product leadership through data-driven recommendations communicating the state of the business, root-causing metric movements, and using experimentation results to influence product and business decisions.
• Implement scalable machine learning algorithms that run in production on large-scale data.
• Run exploratory data analysis to better understand user and business phenomena, and to discover untapped areas of growth and optimization.
• Answer complex analytical questions from large datasets to help shape Careem's products and services.
• Define and track key metrics for specific personalization initiatives.
• Design and run randomized controlled experiments (A/B tests), analyze results, and communicate findings to cross-functional teams.
• Continually challenge the status quo investigating new data processing technologies, retrieval architectures, and learning paradigms, and ensuring the team operates at industry-leading standards.
• Build and deploy retrieval-augmented generation (RAG) systems and other applications of large language models within the personalization stack.

What You'll Need: 

• 6-8 years of experience in data mining, predictive modeling, time series analysis, machine learning, and Big Data methodologies, including transformation and cleaning of structured and unstructured data.
• Advanced degree in a quantitative discipline such as Physics, Statistics, Mathematics, Engineering, or Computer Science.
• Solid experience with deep learning techniques including attention mechanisms, retrieval models, and transformer-based architectures (XFY or similar) applied to ranking or recommendation problems.
•  Working with or evaluating knowledge graphs, graph neural networks, or graph-based retrieval systems is a strong plus. Careem is actively building toward graph-based retrieval for recommendations.
• 2-4 years of industry experience in personalization, recommendation, or search is a MUST. Preferably gained in a product-driven company operating at scale.
• Strong problem-solving and coding skills.
• Solid knowledge of A/B testing methodology, classical ML, and deep learning.
• Solid understanding of recommendations, ranking, and retrieval systems end-to-end.
• Familiarity with or interest in online/streaming learning systems models that adapt within a session rather than relying solely on batch retraining, is a strong plus.
• Proficiency and demonstrated experience in Python, SQL, Spark, and Hive.
• Demonstrated experience with database technologies (e.g. Hadoop, BigQuery, Amazon EMR, Hive, Oracle, SAP, DB2, Teradata, MS SQL Server, MySQL).
• Demonstrated experience with business intelligence and visualization tools (Tableau, MicroStrategy, ChartIO, Qlik); geospatial data processing skills are a plus.

What We'll Provide You: 

We offer colleagues the opportunity to drive impact in the region while they learn and grow. As a full time Careem colleague, you will be able to:

• Work and learn from great minds by joining a community of inspiring colleagues.
• Put your passion to work in a purposeful organization dedicated to creating impact in a region with a lot of untapped potential.
• Explore new opportunities to learn and grow every day.
• Work remotely from any country in the world for 30 days a year with unlimited vacation days per year. 
• Access to healthcare benefits and fitness reimbursements for health activities including gym, health club, and training classes.