Data Science Manager - Applied AI

About the Job

Since launching in Kuwait in 2004, the company has emerged as the leading on-demand food and Q-commerce app for everyday deliveries, offering convenience and reliability to customers across eight countries in the region. The company is committed to harnessing innovative technology to simplify everyday life for customers, optimize operations for restaurants and local shops, and provide riders with reliable earning opportunities.

At the company, we are fostering a high-performance culture, driven by a motivated workforce and a commitment to growth. Our mission is to spread positive vibes, and we are proud to be recognized as a multi great place to work award winner.

About the Applied AI Tribe

The Applied AI Tribe is the company's internal AI engine focused on transforming cutting-edge AI into tangible business value. The team has successfully delivered over 40 AI products across four strategic streams:

• Product & Tech Platform - Embedding AI capabilities into the company's core product and engineering framework.
• Business Automations - Streamlining workflows to save time and money in operations.
• Self-Serve AI Tools - Empowering team members to build and utilize AI independently.
• Training & Onboarding - Upskilling the team to ensure active participation in the AI era.

Our engineering approach emphasizes creating scalable agentic AI systems, integrating continuous evaluation in product development, and utilizing feedback loops to guide our roadmap.

The Role

As Manager, Data Scientist - AI within the Applied AI Tribe, you will lead a team of data scientists responsible for developing and deploying ML and generative AI systems to inform business decisions across the company's product and operations. This role requires you to balance people leadership with technical expertise, enabling you to make sound technical choices while steering your team's roadmap and project delivery.

Your team's work encompasses the entire AI lifecycle, from defining business challenges to deploying and monitoring models in production, with a significant emphasis on leveraging LLMs, agentic systems, and generative AI to automate decision-making and enhance user experiences.

What's On Your Plate?

People Leadership:

• Lead, grow, and retain a team of data scientists, promoting high technical quality and fostering a culture of ownership and career development.
• Collaborate with recruitment to attract top-tier data science and ML talent.
• Mentor team members in machine learning best practices, agentic system design, production engineering, and stakeholder communication.
• Conduct effective team rituals including planning sessions, design reviews, and retrospectives to maintain alignment and momentum.

Technical Strategy & Delivery:

• Translate complex business problems into actionable ML and AI solutions with measurable success criteria aligned with the tribe's mission.
• Own the technical roadmap, prioritizing impactful work in data enrichment, business automation, self-service tools, and agentic features.
• Advocate for harness-first thinking, ensuring infrastructure and evaluation pipelines are established prior to building AI systems.
• Integrate evaluation into every product iteration, utilizing golden datasets and actionable metrics to inform development.
• Oversee the complete ML lifecycle, including data pipelines, feature engineering, model training, and deployment.
• Promote the use of LLMs and generative AI for enriching data and automating decision-making processes.
• Design experiments to rigorously measure the impact of models and products, including A/B and multivariate testing.
• Enhance ML and engineering standards by improving MLOps practices and fostering internal learning opportunities.

Cross-Functional Partnership:

• Collaborate with product managers and business teams to identify and prioritize high-value AI opportunities.
• Effectively communicate with senior stakeholders, from problem definition to results and recommendations.
• Work closely with engineering teams to understand data systems and ensure seamless integration of production models.

Qualifications

What Did We Order?

Technical Experience:

• Extensive expertise in machine learning, generative AI, deep learning, NLP, recommendation systems, and data mining.
• Proficient in frameworks such as Scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, Transformers, with experience in LLM fine-tuning.
• Familiarity with the OpenAI SDK and major LLM provider APIs for building production AI systems.
• Hands-on experience with LangGraph and Hugging Face for designing agentic workflows and deploying models.
• Strong software engineering fundamentals and MLOps practices, including experience with data orchestration tools and SQL/Python proficiency.
• Capability in designing robust data pipelines and conducting deep statistical analysis, including causal inference and predictive methods.
• Familiarity with Google Cloud Platform and BigQuery is a plus.

Qualifications:

• Bachelor's degree in Engineering, Computer Science, or a related field; postgraduate degree is advantageous.
• Minimum of 6 years experience in data science, ML engineering, or generative AI, particularly in production environments.
• At least 2 years of experience managing or leading a data science or ML team, with a successful history of talent development.

Additional Information

Mindset & Ways of Working:

• Excitement for exploring new models or frameworks.
• Ownership mentality regarding cost, latency, and business impact.
• Preference for building prototypes over perfecting designs.
• Comfort with ambiguity and the ability to define new processes.
• Commitment to simplicity, prioritizing value in design and execution.

At the company, success is determined by both results and behaviors. We value excellence in technical skills along with adherence to our Leadership Principles, which shape our decision-making and collaboration. Your achievements contribute to our growth and impact on the community.