Senior Manager - Data & AI Scientist

About the role

EY is looking for an experienced Senior Manager - Data & AI Scientist to join its growing MENA Technology Consulting team in Egypt. You will help shape and deliver innovative Data & AI transformations across Egypt and the wider MENA region.

Key responsibilities

• Lead Data & AI strategy, advanced analytics, GenAI and agentic AI engagements.
• Advise C-suite stakeholders on AI opportunities, use-case prioritization, operating models and transformation roadmaps.
• Lead end-to-end AI delivery, from strategy and architecture through development, deployment and scale.
• Drive client solutioning, proposals, RFP responses and business development.
• Build trusted senior client relationships and grow the Data & AI practice.
• Lead, coach and develop multidisciplinary Data Science and AI Engineering teams.

Required profile

• Bachelor's or Master's degree in Computer Science, AI, Data Science, Engineering, Mathematics, Statistics or a related discipline.
• Proven experience leading complex client engagements and multidisciplinary technical teams.
• Strong C-suite engagement, consulting, communication and stakeholder-management skills.
• Industry experience, preferably in Financial Services, Government & Public Sector, Energy & Utilities, Telecom or Real Estate & Infrastructure.
• Prior Big Four, management consulting or technology consulting experience highly preferred.
• Experience across Egypt and MENA, with readiness to travel across the region.

Required skills

• Machine learning and advanced analytics: predictive modelling, classification, clustering, forecasting, anomaly detection, recommendations and optimization.
• Deep learning and NLP: neural networks, transformers, Arabic NLP, PyTorch and TensorFlow.
• GenAI and agentic AI: LLMs, prompt engineering, RAG, AI agents, orchestration, evaluation, guardrails and model optimization.
• Platforms: Azure OpenAI, Databricks, AWS Bedrock, Google Vertex AI, Hugging Face, LangChain, LlamaIndex and Semantic Kernel.
• Understanding of AI architecture, MLOps/LLMOps, responsible AI and enterprise-scale AI deployment.