Data Scientist (Machine Learning Engineer)

About Capgemini

Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem. The Group reported 2024 global revenues of €22.1 billion.

Job Responsibities:

Machine Learning & AI Solutions

• Design and implement Agentic AI solutions and applications.
• Experience in Data Science, Data Mining, Machine Learning tasks (e.g. feature engineering, model training, evaluation, and deployment).
• Monitor model performance and implement retraining strategies to ensure long-term accuracy and relevance.

Cloud & Data Engineering

• Collaborate with data engineers to ensure robust data preprocessing, transformation, and storage strategies.

MLOps & Automation

• Familiarity with CI/CD pipelines tailored for ML workflows, including automated testing, model versioning, and deployment.

Collaboration & Innovation

• Work closely with cross-functional teams including data scientists, product managers, and business stakeholders to translate business needs into technical solutions.
• Contribute to knowledge sharing and best practices across the team.

Job Requirements:

Technical Skills:

• Strong proficiency in python, data science and machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch) and agentic application development libraries (e.g. LangChain, LangGraph).
• Strong command of SQL and experience with large-scale data processing tools (e.g., Spark, Pandas).
• Familiarity with cloud platforms (Azure, AWS, GCP) and their ML services.
• Familiarity with MLOps practices, including CI/CD, containerization (Docker), and orchestration (Kubernetes).
• Familiarity with Git and collaborative development workflows.

Experience:

• 2-5 years of experience in solving real-world business problems using AI

• Proven track record of deploying and maintaining ML models in production environments.

Communication Skills:

• Excellent written and verbal communication skills to collaborate effectively with cross-functional teams.
• Fluent in Arabic & English, both spoken and written (German is a plus!)
• Able to listen to and understand customer needs and articulate them as technical requirements to the team.

Additional Skills:

• Strong problem-solving abilities and attention to detail.
• Ability to work collaboratively in a team environment.
• Self-motivated with a continuous learning mindset.