Data Science Team Lead at OREDATA, leading teams to resolve data science challenges in a hybrid environment. Joining a digital transformation firm with a strong collaborative culture.
SP
Hybrid Senior Data Scientist – Generative AI
Posted 5 months ago
About the role
- Senior/Staff Data Scientist developing AI for commerce in the Middle East. Architecting systems for merchant and customer AI assistants and content generation.
Responsibilities
- Architect and implement LLM-based agentic systems including the 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).
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.
Job title
Job type
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Degree requirement
No Education Requirement
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