Senior / Staff Data Scientist - Generative AI

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

Job summary

A leading technology company in Saudi Arabia is seeking a Senior/Staff Data Scientist for GenAI to architect intelligent AI systems. The ideal candidate will have over 5 years of experience in applied ML/NLP, with a focus on building and deploying LLM-based systems. Responsibilities include designing customer support tools and collaborating with cross-functional teams to enhance AI capabilities. This role offers a competitive salary and the chance to impact commerce in the region.

Qualifications

• 5+ years of experience in applied ML/NLP, including 2+ years building and deploying LLM-based systems in production.
• Proven impact on LLM products serving 100K+ users or handling 1M+ queries.

Responsibilities

• Architect and implement LLM-based agentic systems including customer support and content generation tools.
• Collaborate with product and UX teams for prompt hierarchies and workflows.
• Mentor junior data scientists and define best practices.

Skills

Fluency in Arabic

Applied ML/NLP

Transformer architectures

Prompt engineering

Systems thinking

Education

Bachelor’s or Master’s degree in Computer Science, Data Science, or a quantitative discipline

Tools

RAG pipelines

Bedrock

SageMaker

Vertex AI

Job description

We're building the future of commerce AI in the Middle East, powering millions of merchants with intelligent assistants and Gen AI that understands Arabic and English fluently. As our Senior/Staff Data Scientist for GenAI, you'll architect the brain behind our merchant and customer AI assistants, content generation suite, and conversational commerce platform.

This isn't just another chatbot role, you'll design cutting-edge agentic AI systems that can reason about inventory, write compelling Arabic marketing copy, troubleshoot merchant issues, and generate insights from millions of transactions. Your work will directly impact how merchants across the region run their businesses.

Responsibilities

• Architect and implement LLM-based agentic systems including customer support troubleshooting, analytics, and content-generation tools.
• Build RAG pipelines combining structured and unstructured sources (knowledge base, FAQs, policies), optimizing for latency, factuality, and grounding.
• Develop evaluation frameworks (LLM-as-a-judge, human-in-the-loop) to measure helpfulness, factual accuracy, coverage, and coherence.
• Collaborate with product and UX teams to define prompt hierarchies, tool-calling logic, and conversational workflows that handle multi-turn reasoning.
• Drive fine-tuning or parameter-efficient adaptation (LoRA, PEFT) of LLMs for commerce-specific reasoning and multilingual support (Arabic & English).
• Contribute to the GenAI Suite: product description generation, email and blog content creation, landing page design, and brand-aware tone control.
• Lead experimentation on new LLM orchestration frameworks (LangGraph, semantic routers) and scalable deployment using Bedrock, SageMaker, or Vertex AI.
• Mentor junior data scientists and define best practices for versioning, prompt evaluation, and observability (e.g., LangFuse, MLflow, Grafana).

• Nice to have: Fluency in Arabic, prior work on multilingual NLP, or marketplace/SaaS platform experience.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science, or a quantitative discipline.
• 5+ years of experience in applied ML/NLP, including 2+ years building and deploying LLM-based systems in production.
• Proven impact: Delivered or scaled LLM products serving 100K+ users or handling 1M+ queries per day.
• Technical depth: Solid understanding of transformer architectures, RAG design and optimization, prompt engineering, and LLM evaluation techniques.
• Systems thinking: Experience with distributed systems, async workflows, vector search, and caching strategies for latency-sensitive workloads.
• Commerce awareness: Familiarity with e-commerce metrics, merchant pain points, and marketplace platform dynamics.
• Communication: Comfortable translating complex ML systems into actionable insights for non-technical and leadership stakeholders.