Manager Data Scientist - AI

Company Overview

Founded in Kuwait in 2004, talabat is a premier on-demand food and quick-commerce application offering dependable and convenient delivery services. Operating across eight countries in the region, talabat has a strong local presence with in-depth insight into the communities it serves. Leveraging cutting-edge technology and expertise, talabat simplifies daily life for customers, enhances operational efficiency for restaurants and shops, and provides reliable income opportunities for delivery riders. With over 6,000 employees, talabat fosters a high-performance culture focused on genuine impact and engagement, earning multiple workplace excellence awards.

Role Context: Applied AI Tribe

The Applied AI Tribe is talabat's internal AI-focused team dedicated to transforming state-of-the-art AI into tangible business outcomes, prioritizing the net AI value delivered. The tribe has launched over 40 AI products organized into four strategic areas:

• Integration of AI functionalities into the core product and technology platform.
• Business workflow automations that optimize time and reduce costs.
• Self-service AI tools empowering employees to utilize AI independently.
• Training and onboarding programs upskilling teams to engage with AI technologies.

This team emphasizes scalable agentic AI systems with embedded evaluation throughout product iterations and feedback loops guiding future developments.

Position Overview

The Manager, Data Scientist - AI will lead a group of data scientists in developing and deploying machine learning and generative AI models that underpin critical decision-making across talabat's offerings and operations. The role combines hands-on technical expertise with leadership responsibilities, owning the delivery roadmap and fostering team growth.

Key Responsibilities

• Lead, expand, and retain a high-performing data science team focused on technical excellence, ownership, and career growth.
• Collaborate with recruitment to onboard top-tier data science and ML talent aligned to team scaling.
• Coach team members on best practices in machine learning, agentic system design, production engineering, and stakeholder engagement.
• Facilitate effective team processes including planning, design reviews, and retrospectives to maintain alignment and velocity.
• Convert ambiguous business challenges into definable ML and AI solutions featuring clear, success-oriented metrics congruent with organizational goals.
• Oversee the team’s technical roadmap prioritizing impactful initiatives in data enrichment, automation, self-service tools, and agentic AI features.
• Advocate harness-first approaches ensuring evaluation systems, observability tools, and infrastructure are integral before agent implementation.
• Implement continuous evaluation methodologies using golden datasets and stakeholder metrics to guide development decisions.
• Manage end-to-end machine learning lifecycle components including data ingestion, feature engineering, model training, deployment, serving, and monitoring.
• Drive adoption of large language models (LLMs) and generative AI technologies for scalable automated decision-making, content analysis, and data enhancement.
• Design and analyze controlled experiments such as A/B and multivariate tests to assess model and product impact rigorously.
• Enhance machine learning and engineering standards by improving MLOps practices, code quality, tooling, and internal educational initiatives.
• Form strategic partnerships with product managers, business teams, and engineering to identify AI opportunities, align roadmaps, and ensure robust production integration.
• Communicate clearly with senior leadership from problem definition through outcome delivery and recommendations.

Required Qualifications and Experience

• Deep knowledge of machine learning, generative AI, deep learning, natural language processing, recommendation systems, and data mining techniques.
• Proficiency with ML and generative AI frameworks such as Scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, Transformers, including fine-tuning large language models.
• Experience using the OpenAI SDK and familiarity with APIs of major LLM providers for designing and managing AI systems in production.
• Hands-on practice with LangGraph for developing multi-step agentic workflows and complex AI orchestrations.
• Utilization of Hugging Face tools for sourcing, fine-tuning, and deploying models.
• Strong understanding of embeddings, vector databases, semantic search, and retrieval approaches to build retrieval-augmented generation (RAG) pipelines and similarity applications.
• Solid foundation in software engineering principles including clean code, algorithms, data structures, and ML system architecture.
• Proven track record of deploying and monitoring ML models in production with strong competence in MLOps methodologies.
• Expertise in data and ML engineering covering pipeline orchestration (e.g., Airflow) and advanced feature engineering.
• Advanced skills in SQL and Python programming complemented by robust statistics knowledge including experimental design, causal inference, and predictive analytics.
• Familiarity with agentic system design concepts such as harness-first methodology, LLM observability, evaluation pipelines, and feedback loops is a distinct advantage.
• Experience with Google Cloud Platform and BigQuery is beneficial.
• Bachelor’s degree in Engineering, Computer Science, or a related discipline; postgraduate qualifications are advantageous.
• Minimum of 6 years’ professional experience in data science, ML engineering, or generative AI with demonstrable experience shipping production models.
• At least 2 years’ experience in leading or managing a data science or machine learning team, with a documented track record in talent development and delivery excellence.
• Background working with ML systems in online consumer product settings is strongly preferred.

Mindset and Work Approach

• Embrace curiosity over certainty by actively exploring novel models and frameworks.
• Take ownership of technical and business impacts such as latency and costs, individually and as a team priority.
• Prioritize experimental builds over exhaustive designs—practical prototypes are valued.
• Thrive in ambiguity by innovating solutions where no prior blueprint exists.
• Adopt simplicity by selecting approaches that balance value and complexity effectively, adhering to the #makeithappen principle.

Additional Notes

talabat emphasizes success measured by both achievement and adherence to core leadership principles which guide decision-making and collaboration. The role requires a commitment to quality craft and impactful leadership that drives the business and delivers outstanding experiences.