Join us in building the intelligence that powers product discovery for millions of shoppers and thousands of merchants across the Middle East. As the Data Science Manager for the Recommendation Systems Pod, you will lead the design and execution of large-scale personalization models that directly impact the company topline.
This is a rare opportunity to shape the next generation of commerce AI in a high-growth market characterized by highly diverse user and merchant behaviors across the GCC.
Responsibilities
Design, train, and deploy recommendations/personalization models leveraging deep learning, sequence models (Transformers, GRU), and boosted trees (XGBoost, LightGBM).
Develop multi-task learning approaches that optimize engagement, conversion, and merchant outcomes simultaneously.
Build scalable retrieval and ranking systems with ANN search (FAISS, ScaNN) and vector embeddings trained on user, product, and event data.
Collaborate with infra to productionize real-time feature pipelines (ClickHouse, Kafka, Spark).
Run A/B tests and interpret results using causal inference and uplift modeling to drive measurable business impact.
Integrate model outputs with platform APIs for dynamic personalization in search, home feeds, and store pages.
Define best practices for offline evaluation (MAP@K, NDCG) and online experimentation metrics (CTR, CVR, GMV uplift).
Partner with product analytics and data science to iterate on signal enrichment and cold-start strategies.
Mentor junior data scientists and define best practices
Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related technical field.
4+ years of hands-on ML experience, including 2+ years designing or deploying large-scale recommendation systems.
Track record: Built or maintained systems serving 1M+ users or generating 100M+ personalized predictions daily.
Deep expertise in representation learning, embeddings, attention mechanisms, and multi-task learning.
Demonstrated success integrating multi-stage ranking systems across e-commerce surfaces (search, feeds, product detail pages) with measurable online lift (CVR, GMV).
Proficient with large-scale data ecosystems: Kafka, Spark, ClickHouse, BigQuery, or equivalent.
Strong understanding of offline/online evaluation metrics, A/B experimentation, and model monitoring frameworks.
Skilled in debugging, optimization, and productionization of ML pipelines in cloud or containerized environments