
Salla
Senior / Staff Data Scientist – Recommendations & Personalization Systems
- Permanent
- Jeddah, Saudi Arabia
- Experience 2 - 5 yrs
Job expiry date: 25/04/2026
Job overview
Date posted
11/03/2026
Location
Jeddah, Saudi Arabia
Salary
SAR 20,000 - 30,000 per month
Compensation
Comprehensive package
Experience
2 - 5 yrs
Seniority
Senior & Lead
Qualification
Bachelors degree
Expiration date
25/04/2026
Job description
The Senior / Staff Data Scientist – Recommendations & Personalization Systems is responsible for leading the design, training, deployment, and optimization of large-scale recommendation and personalization models that power product discovery for millions of users and thousands of merchants across the GCC. The role focuses on deep learning, sequence models (Transformers, GRU), boosted trees (XGBoost, LightGBM), and multi-task learning to simultaneously optimize engagement, conversion, and merchant outcomes. The data scientist develops scalable retrieval and ranking systems using ANN search techniques such as FAISS and ScaNN, leveraging vector embeddings from user, product, and event data. The role involves collaboration with infrastructure teams to productionize real-time feature pipelines using ClickHouse, Kafka, and Spark, integrating model outputs with platform APIs to provide dynamic personalization across search, home feeds, and store pages. Responsibilities include running A/B tests, applying causal inference, uplift modeling, monitoring models in production, and mentoring junior data scientists while defining best practices for offline evaluation metrics (MAP@K, NDCG) and online metrics (CTR, CVR, GMV uplift). The position demands proficiency in large-scale ML pipelines, cloud/containerized deployments, and advanced analytics applied to e-commerce personalization systems.
Required skills
Key responsibilities
- Design, train, and deploy recommendation and personalization models using deep learning, sequence models, and boosted trees
- Develop multi-task learning approaches to optimize engagement, conversion, and merchant outcomes simultaneously
- Build scalable retrieval and ranking systems using ANN search and vector embeddings derived from user, product, and event data
- Collaborate with infrastructure teams to productionize real-time feature pipelines using Kafka, Spark, and ClickHouse
- Integrate model outputs with platform APIs for dynamic personalization in search, home feeds, and store pages
- Run A/B tests and interpret results using causal inference and uplift modeling to drive measurable business impact
- Define and enforce best practices for offline evaluation (MAP@K, NDCG) and online experimentation metrics (CTR, CVR, GMV uplift)
- Mentor junior data scientists and establish best practices for recommendation system development and deployment
Experience & skills
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, or related technical field
- 4+ years of hands-on ML experience, including 2+ years designing or deploying large-scale recommendation systems
- Experience building or maintaining systems serving 1M+ users or generating 100M+ personalized predictions daily
- Deep expertise in representation learning, embeddings, attention mechanisms, and multi-task learning
- Experience integrating multi-stage ranking systems across e-commerce surfaces with measurable online lift
- Proficiency with large-scale data ecosystems such as Kafka, Spark, ClickHouse, or BigQuery
- 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