About the Role / About the Job
Since launching in Kuwait in 2004, the company has been the leading on-demand food and Q-commerce app for everyday deliveries, providing convenience and reliability to customers across eight countries. The Applied AI Tribe at the company is dedicated to turning cutting-edge AI into tangible business value. As the Manager, Data Scientist - AI, you will lead a team responsible for developing and shipping machine learning (ML) and generative AI systems that inform decision-making across the organization. This role combines people leadership with technical expertise, allowing you to guide your team while owning the delivery roadmap.
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Your team's work encompasses the entire AI lifecycle, from defining business problems to deploying models in production, focusing on leveraging large language models (LLMs) and generative AI to enhance decision-making and experiences at scale.
What's On Your Plate?
People Leadership:
- Lead, grow, and retain a team of data scientists, setting high standards for technical quality and supporting career development.
- Collaborate with recruiting to attract top talent in data science and ML.
- Mentor team members in ML best practices, system design, and stakeholder communication.
- Conduct effective team rituals, ensuring alignment and progress.
Technical Strategy & Delivery:
- Translate complex business problems into scoped ML and AI solutions with clear success criteria.
- Own the technical roadmap, prioritizing impactful work across data enrichment, business automation, and AI features.
- Promote harness-first thinking and establish evaluation processes and infrastructure before building on agent logic.
- Implement comprehensive evaluation in product iterations to guide development decisions.
- Oversee the full ML lifecycle: from data pipelines and feature engineering to production deployment and monitoring.
- Promote the use of LLMs and generative AI for data enrichment and automated decisions at scale.
- Design and analyze experiments to assess model and product impact comprehensively.
- Elevate standards in MLOps, code quality, and internal learning throughout the team.
Cross-functional Partnership:
- Collaborate with product managers and business teams to identify and prioritize AI opportunities on the roadmap.
- Communicate effectively with senior stakeholders from problem framing to delivery of results and recommendations.
- Work with engineering teams to ensure reliable data models and smooth integration into production.
Qualifications
What Did We Order?
Technical Experience:
- Expertise in machine learning, generative AI, deep learning, and data mining, with hands-on knowledge of frameworks like Scikit-learn, PyTorch, TensorFlow, etc.
- Proficiency in using OpenAI SDKs and familiarity with LLM provider APIs.
- Experience with LangGraph for agentic workflows and Hugging Face for model deployment.
- Strong understanding of embeddings, vector databases, and semantic search.
- Solid software engineering skills including clean production code and system design.
- Proven experience in MLOps and shipping ML models to production.
- Strong background in data engineering and building data pipelines.
- Excellent SQL and Python skills with a background in statistical methods.
- Familiarity with agentic system design practices is a plus.
- Experience with BigQuery and Google Cloud Platform is a plus.
Qualifications:
- Bachelor’s degree in Engineering, Computer Science, or related field; a postgraduate degree is desirable.
- 6+ years of experience in data science, ML engineering, or generative AI, including direct production experience.
- 2+ years in a leadership role managing data science or ML teams with a successful track record of developing talent.
- Experience with ML systems in online consumer product environments is advantageous.
Mindset & Ways of Working
- Curiosity: Eager to explore new models and frameworks.
- Ownership: Address latency and business impact proactively.
- Bias to build: Prefer prototypes in real workflows over perfect designs.
- Comfortable with ambiguity: Help create solutions where no playbook exists.
- Simplicity: Choose approaches that maximize value with minimal complexity.
At the company, both the results you achieve and your behaviors matter. We prioritize excellence in craft and adhere to Leadership Principles that guide our decision-making and collaboration.
