Engineering Manager - Applied AI (Data Scientist)

About the role

talabat's Applied AI Tribe is an internal AI team focused on turning AI into measurable business value, with a harness-first approach and evaluation embedded in every product. As Manager, Data Scientist - AI, you will lead a team of data scientists building and shipping ML and generative AI systems that power decisions across the product and business, acting as both people leader and technical practitioner.

Key responsibilities

• Lead, grow and retain a team of data scientists, mentor them in ML best practices, agentic system design and production engineering, and partner with recruiting to attract talent.
• Translate ambiguous business problems into scoped ML and AI solutions with clear success criteria, and own the team's technical roadmap.
• Prioritise work across data enrichment, business automation, self-serve tooling and agentic features, and champion harness-first thinking.
• Embed evaluation in every product iteration: golden datasets, stakeholder-aligned metrics and weekly eval jobs.
• Oversee the full ML lifecycle: data pipelines, feature engineering, model training, deployment, serving and monitoring.
• Drive adoption of LLMs and generative AI for data enrichment, smart content understanding and automated decision-making, and design and analyse A/B and multivariate experiments.
• Elevate ML and engineering standards, improve MLOps, code quality and internal learning programmes.
• Partner with product managers and business teams to shape the roadmap, communicate with senior stakeholders, and collaborate with engineering teams on data systems and production integration.

Required profile

• Bachelor's degree in Engineering, Computer Science or a related field; a postgraduate degree is a plus.
• 6+ years of experience in data science, ML engineering and/or generative AI, including shipping models to production.
• 2+ years managing or leading a data science or ML team, with a track record of developing talent.
• Experience building ML systems in an online consumer product environment is a strong plus.

Required skills

• Deep expertise in machine learning, generative AI, deep learning, NLP, recommendation systems and data mining.
• Hands-on experience with Scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, Transformers and LLM fine-tuning.
• OpenAI SDK and major LLM provider APIs, LangGraph for agentic workflows, and Hugging Face.
• Embeddings, dense and sparse retrieval, vector databases and semantic search for RAG pipelines.
• Data pipelines (e.g. Airflow), feature engineering, excellent SQL and Python, and MLOps.
• Familiarity with agentic system design is a strong plus; BigQuery and Google Cloud Platform are a plus.