Manager Data Scientist - AI

About talabat

Role overview

People leadership

• Lead, grow, and retain data scientists; set technical standards, support career development, and recruit talent with the recruiting team.
• Mentor the team in ML best practices, agentic design, production engineering, and stakeholder communication.
• Run team rituals, planning, design reviews, and retrospectives.

Technical strategy and delivery

• Turn ambiguous business problems into scoped solutions with measurable success criteria and own priorities across the team's AI roadmap.
• Build evaluation pipelines, LLM observability, tooling, and infrastructure; use golden datasets, stakeholder-aligned metrics, and weekly evaluation jobs.
• Oversee data pipelines, feature engineering, model training, production deployment, serving, and monitoring.
• Advance LLM and GenAI applications for data enrichment, content understanding, automated decisions, and intelligent experiences.
• Design and analyze A/B and multivariate experiments; improve MLOps, engineering quality, tooling, and team learning.

Cross-functional partnership

• Partner with product managers and business teams to identify AI opportunities and shape the roadmap.
• Communicate problem framing, results, and recommendations to senior stakeholders.
• Work with engineering teams on data systems, reliable data models, and production integration.

Technical qualifications

• Deep expertise in ML, GenAI, deep learning, NLP, recommendation systems, and data mining; hands-on knowledge of Scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, Transformers, and LLM fine-tuning.
• Proficiency with the OpenAI SDK and major LLM provider APIs; hands-on LangGraph experience with stateful multi-step agents and orchestration.
• Hugging Face experience with Transformers, Hub, or Inference API for model sourcing, fine-tuning, and deployment.
• Understand embeddings, dense and sparse retrieval, vector databases, semantic search, and RAG pipelines.
• Strong production software engineering, data structures and algorithms, and ML system design; experience shipping and monitoring production models and MLOps.
• Strong data/ML engineering, data pipeline orchestration such as Airflow, and feature engineering.
• Excellent SQL and Python; strong statistics, experiment design, causal inference, and predictive methods.
• Agentic design, LLM observability, evaluation pipelines, and feedback loops are a plus; BigQuery and Google Cloud Platform are also a plus.

Experience and education

• Bachelor's degree in Engineering, Computer Science, or a related field; a postgraduate degree is a plus.
• At least 6 years in data science, ML engineering, and/or GenAI, including production model delivery.
• At least 2 years managing or leading a data science or ML team and developing talent.
• Online consumer product ML systems experience is a strong plus.

Ways of working