Senior Data Scientist - Recommendation Systems Pod

Salla

About The Position

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.

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.

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.