Manager Data Scientist - AI

Role Overview

Manager Data Scientist - AI at Delivery Hero (talabat), leading the Applied AI Tribe's data science team building and shipping ML and generative AI systems across the region's leading on-demand food and Q-commerce platform.

Company Overview

talabat is the leading on-demand food and Q-commerce app for everyday deliveries, operating in eight countries across the region since launching in Kuwait in 2004. The company serves customers, restaurants, local shops, and riders, employing 6,000+ team members and building a high-performance culture focused on innovation and measurable business value.

The Applied AI Tribe is talabat's internal AI engine, focused on turning cutting-edge AI into genuine, measurable business value with a north star of Net AI Value Delivered. The team has shipped 40+ AI products across four strategic streams: Product & Tech Platform, Business Automations, Self-Serve AI Tools, and Training & Onboarding. The engineering approach centers on shipping agentic AI systems at scale with a harness-first philosophy, evaluation embedded into every product iteration, and production feedback loops that drive the roadmap.

Role Purpose

Lead a team of data scientists building and shipping ML and generative AI systems that power decisions across talabat's product and business. You will balance people leadership with technical practitioner responsibilities, owning the team's roadmap and delivery while staying close enough to the work to make great technical decisions and grow your team.

Key Responsibilities

People Leadership

• Lead, grow, and retain a team of data scientists with a high bar for technical quality and culture of ownership.
• Support each team member's career development and mentor them in ML best practices, agentic system design, production engineering, and stakeholder communication.
• Partner with recruiting to attract top data science and ML talent as the tribe scales.
• Run effective team rituals including planning, design reviews, and retrospectives that keep the team aligned and moving with pace.

Technical Strategy & Delivery

• Translate ambiguous business problems into well-scoped ML and AI solutions with clear, measurable success criteria tied to Net AI Value Delivered.
• Own the team's technical roadmap, prioritizing high-impact work across data enrichment, business automation, self-serve tooling, and agentic product features.
• Champion harness-first thinking and ensure evaluation pipelines, LLM observability, tooling, and infrastructure are in place before agent logic is built.
• Embed evaluation into every product iteration with golden datasets, stakeholder-aligned metrics, and weekly evaluation jobs that guide what gets built, fixed, or retired.
• Oversee the full ML lifecycle including data pipelines, feature engineering, model training, production deployment, serving, and monitoring.
• Drive adoption of LLMs and generative AI for data enrichment, smart content understanding, and automated decision-making at scale.
• Design and analyse experiments (A/B and multivariate) to rigorously measure model and product impact.
• Elevate ML and engineering standards across the team, improving MLOps, code quality, tooling, and internal learning programmes.

Cross-functional Partnership

• Partner with product managers and business teams to identify high-value AI opportunities and shape them into the team's roadmap.
• Communicate clearly with senior stakeholders from problem framing through to results and recommendations.
• Collaborate with engineering teams to understand data systems, build reliable data models, and ensure smooth production integration.

Qualifications & Experience

• Bachelor's degree in Engineering, Computer Science, or a related field; 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 and delivering through others.
• Experience building ML systems in an online consumer product environment is a strong plus.

Skills & Competencies

• Deep expertise in machine learning, generative AI, deep learning, NLP, recommendation systems, and data mining.
• Hands-on knowledge of ML and GenAI frameworks: Scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, Transformers, and LLM fine-tuning.
• Proficiency with the OpenAI SDK and familiarity with major LLM provider APIs for building and orchestrating production AI systems.
• Hands-on experience with LangGraph for building stateful, multi-step agentic workflows and complex agent orchestration patterns.
• Experience with Hugging Face (Transformers, Hub, Inference API) for model sourcing, fine-tuning, and deployment.
• Solid understanding of embeddings, dense and sparse retrieval, vector databases, and semantic search for building RAG pipelines and similarity-based applications.
• Strong software engineering fundamentals including clean production code, data structures and algorithms, and ML system design.
• Proven experience shipping and monitoring ML models in production with solid MLOps practices.
• Strong data and ML engineering skills including building and orchestrating data pipelines (e.g., Airflow) and robust feature engineering.
• Excellent SQL and Python; solid statistical foundations including experiment design, causal inference, and predictive methods.
• Familiarity with agentic system design (harness-first thinking, LLM observability, evaluation pipelines, and production feedback loops) is a strong plus.
• Experience with BigQuery and Google Cloud Platform is a plus.

Additional Information

Mindset & Ways of Working

• Curiosity over certainty: excited to try new models and frameworks.
• Ownership: treat cost, latency, and business impact as your problem and your team's.
• Bias to build: a prototype in a real workflow beats a perfect design doc.
• Comfortable with ambiguity: most work has no playbook yet, and you help write it.
• Keep it simple: pick the approach that delivers the most value with the least complexity.

At talabat, success is defined by both results and behaviors. The company hires for excellence in craft and for Leadership Principles that shape how you think, decide, and collaborate. What you achieve matters, and how you achieve it defines the organization.