Senior Data Scientist - AI and Machine Learning

About talabat

Founded in Kuwait in 2004, talabat is a leading on-demand food and quick-commerce delivery platform operating across eight countries in the region. With a deep understanding of the diverse communities it serves, talabat leverages cutting-edge technology to simplify daily life for customers, optimize partner restaurant and shop operations, and offer reliable income opportunities for its delivery riders. The organization is committed to cultivating a high-performance and engaged workforce, earning recognition as a multi-time "Great Place to Work" award winner with a community of over 6,000 employees.

Role Overview

This role presents the opportunity to impact millions of users, restaurant partners, and riders by advancing talabat's platform intelligence. As a Senior Data Scientist focused on AI and ML within talabat's global AI hub, you will design, develop, and deploy machine learning and generative AI solutions that influence product innovation and business strategies. You will manage a specific domain end-to-end in collaboration with product and business leaders, overseeing the full machine learning lifecycle including problem framing, data modeling, feature engineering, training, deployment, serving, and ongoing monitoring of models in production. A key focus will be on utilizing Generative AI and Large Language Models (LLMs) to enhance data enrichment, content understanding, and automated decision-making at scale.

Key Responsibilities

• Translate ambiguous business challenges into well-defined machine learning problems with measurable success metrics.
• Deliver impactful insights and data-driven recommendations through rigorous analysis and automated reporting to guide strategic decisions.
• Design, implement, and operate end-to-end machine learning and generative AI systems, including data pipelines, feature engineering, model lifecycle management, and monitoring in production environments.
• Lead engineering-intensive tasks by architecting robust ML systems, writing maintainable and scalable production code, and sustaining reliable models that address real-world business demands.
• Train, evaluate, and optimize models by selecting efficient algorithms and architectures that maximize business value.
• Exploit LLMs and generative AI technologies for enhancing data quality, intelligent content processing, and automated decision workflows within production services.
• Develop and maintain data models, features, and pipelines crucial for model training and performance analysis in your domain.
• Design, execute, and analyze experiments such as A/B and multivariate tests to assess product and model effectiveness.
• Gain comprehensive knowledge of the source data and generation systems through thorough documentation, collaboration, and data profiling.
• Collaborate with product and business teams to discover key opportunities and convert them into actionable machine learning solutions.
• Mentor fellow data scientists to support their professional development.
• Promote ML and software engineering best practices by enhancing workflows, tooling, MLOps approaches, and internal educational programs.

Required Qualifications and Experience

• Extensive expertise in machine learning, generative AI, deep learning, recommendation algorithms, natural language processing, pattern recognition, and data mining.
• Proficient with machine learning and Generative AI frameworks and libraries such as Scikit-learn, XGBoost, LightGBM, CatBoost, Support Vector Machines, Keras, TensorFlow, PyTorch, Transformers, and fine-tuning of LLMs.
• Strong foundation in software engineering including coding best practices, understanding of data structures and algorithms, and experience in designing ML systems and general system architectures.
• Hands-on experience deploying, serving, and monitoring ML models in production, complemented by knowledge of MLOps methodologies.
• Advantageous skills in data and ML engineering such as orchestrating data and training workflows with technologies like Airflow and advanced feature engineering.
• Proficiency in SQL and executing reproducible analysis and modeling tasks using Python.
• Solid grounding in statistics, including experimental design, causal inference, predictive modeling, and techniques for A/B and multivariate testing.
• Understanding of data modeling and dimensional database design.
• Strong command over the complete ML lifecycle from problem statement to model deployment, result interpretation, and communication.
• Experience with product analytics data including impressions, event tracking, and metrics like conversion rates, engagement, and retention.
• Prior exposure to LLMs and NLP solutions aimed at data enrichment and automation considered a plus.
• Familiarity with BigQuery and Google Cloud Platform is beneficial.
• A bachelor's degree in engineering, computer science, technology, or related fields is required; postgraduate qualifications are an advantage but not mandatory.
• At least 5 years of professional experience combining data science, machine learning engineering, and generative AI with a track record of deploying ML models in production environments.
• Experience developing ML solutions in consumer-facing online products is desirable.
• A problem-solving mindset coupled with a proactive attitude towards learning and collaboration.
• Excellent communication skills and a strong sense of responsibility and simplicity in delivering impactful results.