Staff Data Scientist

About talabat

Founded in Kuwait in 2004, talabat is a leading on-demand food and quick-commerce platform that helps people with everyday deliveries. The company operates across eight countries in the region and focuses on convenience, reliability, and a strong understanding of local customer needs.

talabat uses technology and domain knowledge to make daily life easier for customers, improve operations for restaurant partners and local shops, and create dependable earning opportunities for riders. The organization is building a high-performance culture with an engaged workforce and strong talent density, and its 6,000+ team members are united around creating positive impact. The company has also received multiple great place to work awards.

Role Overview

As a Staff Data Scientist on the global AI hub, you will help shape how the platform becomes smarter in understanding and serving users. The role focuses on designing, developing, and delivering machine learning and generative AI solutions that influence product and business decisions.

You will own a domain from end to end and work closely with product and business leaders, as well as data scientists and machine learning engineers. The role covers the complete ML lifecycle, including problem definition, data preparation, feature creation, model training, deployment, serving, and production monitoring. A major part of the work will involve GenAI and large language models for tasks such as data enrichment, content understanding, and automated decision-making at scale.

Key Responsibilities

The role combines analytical thinking with hands-on engineering and production ownership.

• Convert complex business challenges into clearly defined machine learning and data science problems with measurable success criteria.
• Deliver actionable insights and data-backed recommendations through strong analysis and automated reporting to support strategic decisions.
• Develop, launch, and support end-to-end ML and generative AI systems in production, including pipelines, features, training, serving, and monitoring.
• Handle engineering-intensive work from architecture to implementation by writing scalable production code and maintaining reliable ML systems.
• Train, evaluate, and refine models while choosing the most suitable and practical algorithms or architectures for business impact.
• Use LLMs and generative AI to improve data enrichment, content intelligence, and automated decision workflows.
• Build and maintain the data models, features, and pipelines that support training and performance measurement.
• Plan and analyze experiments, including A/B and multivariate testing, to assess product and model outcomes.
• Learn source data and upstream systems deeply through documentation, collaboration with engineering teams, and structured profiling.
• Work with product and business stakeholders to identify high-value opportunities and turn them into ML solutions and recommendations.
• Mentor other data scientists and support their growth.
• Help improve engineering and ML standards, tooling, MLOps practices, and internal learning programs.

Requirements

• Strong expertise in machine learning, generative AI, deep learning, recommendation systems, NLP, pattern recognition, and data mining.
• Hands-on experience with ML and GenAI tools and frameworks such as Scikit-learn, XGBoost, LightGBM, CatBoost, SVMs, Keras, TensorFlow, PyTorch, Transformers, and LLM fine-tuning.
• Solid software engineering fundamentals, including strong coding ability, understanding of data structures and algorithms, and experience with general system design and ML system design.
• Proven track record of building, deploying, serving, and monitoring ML models in production with strong MLOps knowledge.
• Experience in data and ML engineering, including pipeline orchestration tools such as Airflow and strong feature engineering skills.
• Excellent SQL and strong Python skills for reproducible analysis and modeling.
• Good statistical grounding, including experiment design, A/B and multivariate testing, and inferential, causal, and predictive methods.
• Familiarity with data modeling and dimensional design.
• Comfort with the full ML lifecycle, from problem framing and data auditing through modeling, deployment, interpretation, and presentation.
• Understanding of product data such as impressions and events, along with product health metrics like conversion, engagement, and retention.
• Exposure to LLM and NLP-based automation for data enrichment and smart workflows is an advantage.
• Knowledge of BigQuery and Google Cloud Platform is a plus.
• Bachelor’s degree in engineering, computer science, technology, or a related field; a postgraduate degree is beneficial but not mandatory.
• At least 7 years of experience spanning data science, machine learning engineering, and generative AI, including production model delivery.
• Experience building ML systems for online consumer products is preferred.
• Strong problem-solving ability with a practical, resourceful mindset.
• Excellent collaboration and communication skills.
• Strong ownership, accountability, and a preference for simple, effective solutions.

Additional Information

This position is based in Dubai, United Arab Emirates and is a full-time onsite role.