Data Scientist

Company Details

Careem Grocery is redefining how people shop for groceries, pharmacy, and retail products in the Middle East. As an innovative vertical within Careem, the region's leading super app, Careem Grocery aims to simplify lives by offering a seamless, fast, and reliable shopping experience. With a focus on quality, trust, and convenience, Careem Grocery is committed to delivering essentials swiftly, ensuring customers have more time for what truly matters.

Job Roles & Responsibilities

Job Title: Senior Data Scientist

What You'll do

• Work closely with engineers and data teams to design, build, and deploy scalable machine learning models that power Careem’s platform.
• Analyze large-scale datasets to uncover insights, optimize logistics algorithms, and improve user experiences across ride-hailing, food delivery, and payments.
• Develop, test, and maintain robust ML systems using Python and modern ML frameworks, ensuring high performance and reliability.
• Collaborate with cross-functional teams — product, engineering, operations — to align AI-driven solutions with business goals.
• Train, validate, and deploy models in cloud environments ensuring scalability and efficiency.
• Conduct peer reviews, uphold coding best practices, and contribute to building a strong data science culture at Careem.
• Continuously research and experiment with emerging AI/ML techniques to drive innovation in Careem’s super app ecosystem.
• Monitor deployed models, troubleshoot issues, and enhance performance within defined SLAs.
• Participate in team agile practices and contribute to the continuous evolution of Careem’s data-driven decision-making.

Ideal candidate profile

• 5+ years of professional experience in data science, applied machine learning, or related fields.
• Bachelor’s/Master’s degree in Computer Science, Statistics, Mathematics, or a related technical field.
• Strong proficiency in Python and hands-on experience with ML libraries (TensorFlow, PyTorch, Scikit-learn, etc.).
• Solid knowledge of machine learning fundamentals: supervised/unsupervised learning, feature engineering, model evaluation, and deployment.
• Experience handling large datasets using SQL and big data platforms (Spark, Hadoop, or similar).
• Understanding of cloud platforms (AWS, GCP, or Azure) for model training and deployment.
• Familiarity with containerization technologies (Docker, Kubernetes) for scalable deployment.
• Strong problem-solving skills and ability to translate business challenges into data-driven solutions.
• Experience applying professional software engineering practices, including version control, testing, and CI/CD pipelines.
• Excellent communication and collaboration skills to work effectively in cross-functional teams.