Applied AI Manager
Role Overview
The role is described as Manager, Data Scientist - AI within the Applied AI Tribe.
The manager leads data scientists building and shipping ML and generative AI systems for product and business decisions.
The role combines people leadership and hands-on technical work, with ownership of the team's roadmap and delivery.
Work spans problem framing, data modelling, feature engineering, model training, deployment, and production monitoring.
Key Skills for This Role
Full Job Posting
About talabat and the Applied AI Tribe
talabat is an on-demand food and Q-commerce delivery company operating across eight countries in the region.
The Applied AI Tribe focuses on measurable business value and has shipped more than 40 AI products across product and technology platforms, business automation, self-serve tools, and training.
The team builds agentic AI systems at scale, with evaluation and production feedback informing product development.
Role overview
The role is described as Manager, Data Scientist - AI within the Applied AI Tribe.
The manager leads data scientists building and shipping ML and generative AI systems for product and business decisions.
The role combines people leadership and hands-on technical work, with ownership of the team's roadmap and delivery.
Work spans problem framing, data modelling, feature engineering, model training, deployment, and production monitoring.
People leadership and delivery
- Lead, grow, and retain a data science team; support recruiting, career development, mentoring, and effective team rituals.
- Translate ambiguous business problems into measurable ML and AI solutions and own prioritization across the team's roadmap.
- Oversee evaluation pipelines, observability, tooling, metrics, and recurring evaluation jobs.
- Manage the full ML lifecycle, including data pipelines, feature engineering, training, deployment, serving, and monitoring.
- Drive LLM and generative AI use for data enrichment, content understanding, and automated decisions; measure impact through experiments.
- Improve MLOps, code quality, engineering practices, and internal learning; partner with product, business, and engineering teams.
Technical experience
- Deep expertise in machine learning, generative AI, 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 for agentic workflows; Hugging Face experience.
- Knowledge of embeddings, dense and sparse retrieval, vector databases, semantic search, and RAG pipelines.
- Strong production software engineering, data structures and algorithms, ML system design, and experience shipping and monitoring models.
- Strong data and ML engineering, including pipelines, orchestration, and feature engineering; excellent SQL and Python.
- Statistical foundations in experiment design, causal inference, and predictive methods.
Experience and education
- 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 record of developing talent and delivering through others.
- A bachelor's degree in Engineering, Computer Science, or a related field is required; a postgraduate degree is a plus.
- Experience building ML systems in an online consumer product environment is a strong plus.
Additional strengths
- Familiarity with agentic system design, harness-first thinking, LLM observability, evaluation pipelines, and production feedback loops is a strong plus.
- BigQuery and Google Cloud Platform experience is a plus.
Mindset and ways of working
The employer values curiosity, ownership of cost, latency, and business impact, and a bias toward building practical prototypes.
The role calls for comfort with ambiguity and choosing simple approaches that deliver value.
The employer says success is evaluated by both results and behaviors, including its leadership principles.
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