Senior Data Scientist – Recommendation Systems

This is a fully remote position, open to applicants in Saudi Arabia.

📋 Description

• Be part of a team that is focused on developing the intelligence driving product discovery for millions of consumers and numerous merchants throughout the Middle East.

• Take charge of the design and implementation of large-scale personalization models that have a direct effect on the company's revenue.

• Create, train, and deploy recommendation/personalization models utilizing deep learning, sequence models (Transformers, GRU), and boosted trees (XGBoost, LightGBM).

• Develop a multi-objective ranking system that combines engagement, conversion, and merchant value into a singular ranking score (value model), employing multi-task learning to leverage shared representations.

• Construct scalable two-stage retrieval and ranking systems — ANN retrieval (FAISS, ScaNN) over user/product/event embeddings that feed learning-to-rank models (pointwise, pairwise, and listwise objectives).

• Collaborate with infrastructure teams to implement real-time feature pipelines (ClickHouse, Kafka, Spark).

• Establish serving-time impression and feature logging to mitigate training-serving discrepancies and generate unbiased training data.

• Design and conduct online experiments with stringent guardrail metrics; adjust for position and presentation bias in recorded data; utilize counterfactual/off-policy evaluation and uplift modeling to accurately attribute lift.

• Integrate model outputs with platform APIs to facilitate dynamic personalization in search results, home feeds, and store pages.

• Set standards for offline evaluation (MAP@K, NDCG) and online experimentation metrics (CTR, CVR, GMV uplift).

• Collaborate with product analytics and data science teams to refine signal enrichment and cold-start strategies.

• Mentor junior data scientists and establish best practices.

⛳️ Requirements

• Bachelor's or Master's degree in Computer Science, Machine Learning, or a related technical field.

• Over 4 years of practical ML experience, with at least 2 years in designing or deploying large-scale recommendation systems.

• Proven track record: Developed or maintained systems that serve over 1 million users or generate more than 100 million personalized predictions daily.

• Extensive knowledge in representation learning, embeddings, attention mechanisms, and multi-task learning.

• Demonstrated experience integrating multi-stage ranking systems across various e-commerce platforms (search, feeds, product detail pages) with measurable online improvements (CVR, GMV).

• Proficient in large-scale data ecosystems: Kafka, Spark, ClickHouse, BigQuery, or similar technologies.

• Strong understanding of experimental rigor: guardrail metrics, position-bias correction, off-policy/counterfactual evaluation, and model monitoring.

• Expertise in debugging, optimizing, and deploying ML pipelines in cloud or containerized settings.

🏝️ Benefits

• Competitive salary and performance-based bonuses.

• Comprehensive health, dental, and vision insurance.

• Opportunities for professional development and continuous learning.

• Flexible work hours and remote work options.

• Engaging work environment with a diverse team.