Senior Data Scientist Recommendation Systems Pod📣 إعلان

في Salla

منذ ساعتين تقريبا

	نوع العقد	دوام كامل
	طبيعة الوظيفة	بالموقع
	الموقع	مكة المكرمة

وصف الوظيفة

About the Role

Salla is seeking a Senior Data Scientist to join the Recommendation Systems Pod in Makkah. This role is responsible for building the intelligence that powers product discovery for millions of shoppers and thousands of merchants across the Middle East. You will lead the design and execution of large-scale personalization models that directly impact the company's topline in a high-growth market characterized by diverse user and merchant behaviors across the GCC.

Key Responsibilities

Design, train, and deploy recommendation and personalization models using deep learning, sequence models (Transformers, GRU), and boosted trees (XGBoost, LightGBM).
Develop multi-objective ranking systems that integrate engagement, conversion, and merchant value into a single ranking score, utilizing multi-task learning where applicable.
Build scalable two-stage retrieval and ranking systems, including ANN retrieval (FAISS, ScaNN) over embeddings feeding into learning-to-rank models.
Collaborate with infrastructure teams to productionize real-time feature pipelines using technologies like ClickHouse, Kafka, and Spark.
Define serving-time impression and feature logging to mitigate training-serving skew and generate unbiased training data.
Design and conduct online experiments with rigorous guardrail metrics, correcting for position and presentation bias in logged data, and applying counterfactual/off-policy evaluation and uplift modeling for accurate lift attribution.
Integrate model outputs with platform APIs for dynamic personalization across 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 teams to iterate on signal enrichment and cold-start strategies.
Mentor junior data scientists and define best practices for the team.

Required Qualifications

Bachelor's or Master's degree in Computer Science, Machine Learning, or a related technical field.
A minimum of 4+ years of hands-on Machine Learning experience, with at least 2+ years focused on designing or deploying large-scale recommendation systems.
A proven track record of building or maintaining systems serving over 1 million users or generating over 100 million personalized predictions daily.
Deep expertise in representation learning, embeddings, attention mechanisms, and multi-task learning.
Demonstrated success in integrating multi-stage ranking systems across e-commerce surfaces (search, feeds, product detail pages) with measurable online lift.
Proficiency with large-scale data ecosystems such as Kafka, Spark, ClickHouse, or BigQuery.
Strong command of experimentation rigor, including 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.