Data Science Manager - Recommendation Systems

Salla; Makkah Region; On-site; SAR 200,000 - 300,000; Full time

Job summary

A leading e-commerce platform in Saudi Arabia is seeking a Data Science Manager to define their recommendation systems strategy. This pivotal role involves building and leading a team to innovate in personalization technologies, focusing on measurable business outcomes. Ideal candidates have extensive experience in machine learning and a background in team leadership.

Qualifications

• 6+ years in applied ML, with 3+ years leading recommendation systems.
• Hands-on understanding of personalization architectures.
• Experience in driving online lift through A/B experimentation.

Responsibilities

• Define and execute the roadmap for personalization systems.
• Oversee development of recommendation architectures.
• Manage and mentor data science team.

Skills

Applied machine learning

Team leadership

A/B experimentation

Collaboration

Communication

Education

Bachelor's or Master’s in Computer Science or a related field

Tools

Kafka

Spark

ClickHouse

Job description

Join us at the ground floor of building the intelligence that powers discovery for millions of shoppers and thousands of merchants across the Middle East.

As the founding Data Science Manager for the Recommendation Systems Pod, you’ll be both an architect and a builder, defining the technical roadmap, assembling the team, and shipping the first generation of models that will shape how users discover, explore, and buy across our platform.

This is a rare zero-to-one opportunity to design the backbone of a large-scale personalization ecosystem in a region with distinct challenges and rich complexity - multilingual search, fast-evolving consumer behavior, and sharp seasonality across GCC markets. You’ll drive measurable topline growth while laying the foundations of a high-performance applied ML organization.

Responsibilities

• Own the Recommendation Roadmap: Define and execute the long-term technical and business strategy for retrieval, ranking, and personalization systems across the platform (search, home feed, and store pages).
• Model Innovation: Oversee development of state-of-the-art recommendation architectures — multi-task learning, sequence-based models, and hybrid (deep + boosted) rankers. Drive measurable lift in engagement, CVR, and GMV.
• Lead and Grow the Pod: Manage and mentor a cross-functional team of data scientists, ML engineers, and data engineers. Foster technical excellence, iterative experimentation, and accountability for impact.
• Cross-Functional Leadership: Partner with Product, Infra, and Merchant Success to align modeling with business priorities. Translate ambiguous goals into clear, data-backed roadmaps.
• Scalable Deployment & Experimentation: Lead productionization of models with real-time feature stores (ClickHouse, Kafka, Spark), and ensure rigorous A/B testing and causal inference for model launches.
• Quality and Governance: Define and enforce model evaluation, monitoring, and retraining standards. Maintain consistency in feature usage, labeling, and performance reporting across surfaces.
• Collaboration with GenAI: Partner with the GenAI team to infuse recommendation intelligence into conversational shopping experiences and merchant assistants.
• Clear communicator capable of aligning technical and business teams around a common vision.

Qualifications

• Education: Bachelor’s or Master’s in Computer Science, Machine Learning, or a related quantitative field.
• Experience: 6+ years in applied ML, including 3+ years building or leading large-scale recommendation systems.
• Leadership: Proven experience building or growing a team of applied data scientists and delivering end-to-end projects with measurable business outcomes.
• Technical Depth: Hands-on understanding of multi-stage ranking, embeddings, and personalization architectures; able to review and guide model design.
• Experience driving online lift through A/B experimentation.
• Familiarity with data and serving infrastructure (Kafka, Spark, ClickHouse, SageMaker, etc).
• Bonus: Experience in multilingual or Arabic e-commerce contexts.