Senior Data Scientist – Recommendation Systems
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📋 Description
• Join us in building the intelligence that powers product discovery for millions of shoppers and thousands of merchants across the Middle East. • Lead the design and execution of large-scale personalization models that directly impact the company topline. • Design, train, and deploy recommendations/personalization models leveraging deep learning, sequence models (Transformers, GRU), and boosted trees (XGBoost, LightGBM). • Develop multi-objective ranking that blends engagement, conversion, and merchant value into a single ranking score (value model), using multi-task learning where shared representations help. • Build scalable two-stage retrieval and ranking systems — ANN retrieval (FAISS, ScaNN) over user/product/event embeddings feeding learning-to-rank models (pointwise, pairwise, and listwise objectives). • Collaborate with infra to productionize real-time feature pipelines (ClickHouse, Kafka, Spark). • Define serving-time impression and feature logging to eliminate training-serving skew and produce unbiased training data. • Design and run online experiments with rigorous guardrail metrics; correct for position and presentation bias in logged data; apply counterfactual/off-policy evaluation and uplift modeling to attribute lift accurately. • 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.
🎯 Requirements
• 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 command of experimentation rigor: guardrail metrics, position-bias correction, off-policy/counterfactual evaluation, and model monitoring. • Skilled in debugging, optimization, and productionization of ML pipelines in cloud or containerized environments.
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