Applied AI Manager – Lead Data Science Team

About the role

As the Applied AI Manager within talabat's Applied AI Tribe, you will lead a team of data scientists to design, build, and ship machine learning and generative AI solutions that drive business value across the platform.

Key responsibilities

• Lead, grow and retain a high‑performing data science team, setting technical standards and fostering a culture of ownership.
• Translate ambiguous business problems into scoped AI projects with clear success metrics tied to Net AI Value Delivered.
• Own the technical roadmap, prioritising work across data enrichment, business automation, self‑serve tools and agentic product features.
• Ensure harness‑first thinking, embedding evaluation pipelines, LLM observability and production feedback loops.
• Oversee the full ML lifecycle: data pipelines, feature engineering, model training, deployment, serving and monitoring.
• Drive adoption of LLMs, generative AI and agentic systems to automate decisions and enrich data at scale.

Required profile

• 6+ years of experience in data science, ML engineering or generative AI, with a track record of shipping models to production.
• 2+ years of people‑management experience leading data science or ML teams.
• Bachelor’s degree in Engineering, Computer Science or related field; postgraduate degree a plus.
• Experience building ML systems for consumer‑facing products is a strong plus.

Required skills

• Machine learning, deep learning, NLP, recommendation systems, data mining.
• Frameworks: Scikit‑learn, XGBoost, LightGBM, PyTorch, TensorFlow, Transformers.
• LLM tooling: OpenAI SDK, major LLM provider APIs, LangGraph, Hugging Face (Transformers, Hub, Inference API).
• Embeddings, vector databases, semantic search, RAG pipelines.
• Software engineering fundamentals, clean production code, data structures, algorithms.
• MLOps practices, model monitoring, Airflow, SQL, Python.
• BigQuery and Google Cloud Platform (preferred).