About noon

noon, the region's leading consumer commerce platform. On December 12th, 2017, noon launched its consumer platform in Saudi Arabia and the UAE, expanding to Egypt in February 2019. The noon ecosystem of services now marketplaces for food delivery, quick commerce, fintech, and fashion. noon is a work in progress; we’re six years in, but only 5% done.

noon’s mission: Every door, every day

About noon Food

Noon Food is a restaurant-first platform on the noon app, offering fair commissions, a sustainable program, and operating flexibility for the food and beverage industry in the Middle East. The most recent addition to the noon ecosystem, the noon Food mission is to revolutionize the F&B delivery landscape, firmly putting the control and success back in the hands of retailers.

Role Overview

We are seeking a Data Scientist to build and scale data-driven and AI-powered systems that directly impact product and business outcomes. This is a hands-on role focused on applied machine learning and Large Language Model (LLM) systems—not just analysis or experimentation.

You will work end-to-end: framing problems, building models, developing LLM applications (RAG pipelines, agents), and partnering with engineering to take solutions into production.

Key Responsibilities

• Frame ambiguous product or business problems into clear data science or ML objectives.
• Build, train, and evaluate machine learning models (classification, regression, clustering, forecasting, recommendations).
• Design and implement LLM-based systems, including:
• Retrieval-Augmented Generation (RAG) pipelines
• Vector embeddings, semantic search, and ranking
• Prompt engineering, chaining, and agent-based workflows (e.g., LangChain or similar frameworks)
• Combine classical ML with LLMs where appropriate to balance accuracy, cost, latency, and interpretability.
• Partner with engineering to deploy models and LLM systems into production and monitor performance and drift.
• Communicate trade-offs, results, and recommendations clearly to technical and non-technical stakeholders.

Required Qualifications

• 2+ years of experience in Data Science or Machine Learning roles in product or technology-driven environments.
• Python: Strong hands-on experience (pandas, numpy, scikit-learn; production-grade notebooks and scripts).
• Machine Learning: Solid understanding of model selection, evaluation, and feature engineering.
• LLM Systems (Hands-on):
• Proven experience building RAG pipelines using vector databases.
• Hands-on work with frameworks such as LangChain, LlamaIndex, or similar.
• Experience with embeddings, chunking strategies, retrieval, and prompt design.
• SQL: Strong ability to work with large analytical datasets.
• Strong understanding of experimentation, validation, and real-world model limitations.

Nice to Have

• Experience deploying ML or LLM systems in production environments.
• Familiarity with MLOps, model monitoring, or feature stores.
• Experience with unstructured data (text, documents, feedback, logs).
• Background in recommendation systems, search, ranking, or optimization.

What This Role Is Not

• Not a reporting or dashboard-focused role.
• Not a research-only or academic ML role.
• This role is focused on shipping practical ML and LLM-powered systems.