Head of Data Science

Client of Discovered MENA

You will lead the intelligence layer of our marketplace, powering personalization, search/ranking, pricing, demand forecasting, inventory optimization, marketing science, and real-time agentic AI. You ll partner with Product, Engineering, Marketing, Supply Chain, and Commercial to turn data into measurable business outcomes.

What You Will Lead

1. Strategy & Leadership
• Own and execute the data science strategy tied to growth KPIs (acquisition, activation, AOV, repeat rate, margin).
• Build a roadmap balancing fast iteration with long-term foundations (feature store, real-time inference, experimentation).
• Hire and develop a multi-disciplinary DS/ML/MLOps team.
2. Customer Insights & Personalization
• Deliver multi-objective personalization across web/app surfaces.
• Build recommenders, search relevance, semantic search, and LTR models.
3. Pricing & Merchandising Science
• Lead dynamic pricing, elasticity models, and competitive price-matching.
• Optimize promotions, assortment, and attribute coverage.
• Apply causal inference for pricing/promo impact.
4. Forecasting & Inventory Optimization
• Build multi-layer forecasting models for buying and replenishment.
• Develop availability, stockout, returns/refund, and supply-chain efficiency models
5. Marketing Science & Experimentation
• Own full-funnel attribution, incrementality, and ROAS optimization.
• Lead always-on experimentation with rigorous guardrails.
• Deliver LTV, CAC, churn, and audience segmentation models.
6. Agentic AI & Automation
• Build real-time agentic systems for merchandising, pricing, and operations.
• Implement human-in-the-loop workflows and feedback loops for continuous learning.
7. Catalog Quality & Trust
• Apply CV/NLP for enrichment, duplication, attribute extraction, and size mapping.
• Build fraud/abuse detection with explainability and review layers.
8. Data Platform, MLOps & Governance
• Collaborate with Engineering to scale the lakehouse, feature store, and streaming ecosystem.
• Implement mature MLOps (CI/CD, registries, canary/shadow deployments, monitoring).
• Champion governance, privacy, and model risk practices.

Technical Skills

• Strong Python/R/SQL and deep expertise in ML, DL, NLP, forecasting.
• Experience with TensorFlow/PyTorch, Spark/Hadoop, and cloud platforms (AWS/GCP/Azure).
• Solid grounding in experimentation, causal inference, and statistical modeling.