Senior Data Scientist - Recommendation Systems Pod
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Key skills for this role
About the Role
Join us in building the intelligence that powers product discovery for millions of shoppers and thousands of merchants across the Middle East. As a Senior Data Scientist for the.
Key Skills for This Role
Responsibilities
- 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 using multi task learning
- Build scalable two stage retrieval and ranking systems: ANN retrieval (FAISS, ScaNN) over user/product/event embeddings feeding learning to rank models
- 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; apply counterfactual/off policy evaluation and uplift modeling
- 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
- Experience designing, training, and deploying recommendations/personalization models using deep learning, sequence models, and boosted trees
- Experience building scalable two stage retrieval and ranking systems with ANN retrieval and learning to rank
- Experience with real time feature pipelines (ClickHouse, Kafka, Spark)
- Experience designing and running online experiments with guardrail metrics and bias correction
- Ability to mentor junior data scientists and define best practices
Full Job Posting
Overview
- Join us in building the intelligence that powers product discovery for millions of shoppers and thousands of merchants across the Middle East.
- As a Senior Data Scientist for the Recommendation Systems Pod, you'll 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 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.
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