Data Scientist ML,

• Design, develop, and productionize AI/ML models that drive measurable business impact.
• Manage the full AI/ML lifecycle: framing business problems, shaping and preparing datasets, model training, and deployment using MLOps best practices.
• Monitor model performance post-launch to ensure accuracy, reliability, and alignment with business objectives.
• Translate complex business requirements into actionable AI solutions.
• Promote transparency across key AI/ML processes and performance indicators.
• Apply expertise in deep learning, statistical methods, and Responsible AI principles, especially in regulated industries such as aviation or transportation.
• Use tools such as Python, R, SQL, TensorFlow, PyTorch, Keras, scikit-learn, Hugging Face, Docker, Kubernetes, Git CI/CD, and Streamlit for end-to-end ML development and deployment.

Data Scienti...