Data Scientist

CONTEXT

Our client, a major player in its sector, has created a centre of excellence where artificial intelligence and cybersecurity are not side projects but core strategic capabilities. The mission is to transform ambitious AI use cases into production-grade applications that create measurable business value, from the first workshop to the final deployment. A medior data scientist joins a multidisciplinary team of experts who blend data science, engineering, cloud, and security. The story is about moving beyond prototypes: taking a problem, shaping the right machine learning solution, and making it live at scale. Every use case is a chance to explore state-of-the-art generative AI while keeping robustness, ethics, and business impact in view.

ACTIVITIES

• Work end-to-end on AI use cases: problem definition, data exploration, solution design, modelling, implementation, integration, and deployment.
• Select appropriate machine learning and generative AI approaches based on business needs, data maturity, and technical constraints.
• Build scalable AI applications that can be integrated into existing production environments.
• Collaborate with data engineers, software developers, cybersecurity experts, and business stakeholders.
• Evaluate model performance, monitor behaviour, and improve solutions after deployment.
• Communicate findings and recommendations clearly to both technical and non-technical audiences.
• Contribute to reusable assets, best practices, and the continuous improvement of the AI delivery lifecycle.

EXPECTED ACHIEVEMENTS

• Turn high-value AI opportunities into working production applications rather than isolated experiments.
• Improve the reliability, scalability, and business relevance of machine learning solutions.
• Accelerate the centre of excellence's ability to deliver generative AI use cases with confidence.
• Strengthen collaboration between AI, engineering, and cybersecurity teams.
• Create measurable impact through better decisions, automation, and intelligent products.

ENVIRONMENT

• State-of-the-art machine learning and generative AI technologies.
• End-to-end AI delivery, from discovery and experimentation to deployment and monitoring.
• Cloud-native and scalable application architectures.
• Data engineering, MLOps, and production integration practices.
• Cybersecurity-aware AI development.
• Cross-functional teamwork with business, engineering, and specialist experts.