Data & Analytics Chapter Lead

Role Overview: As a Data & Analytics Chapter Lead at KONE, you will provide technical leadership to data scientists and data engineers. Your main responsibility will be to foster skill development and best practices within the team. You will collaborate closely with business, product, and engineering teams to identify challenges and design scalable AI/ML solutions. Developing, optimizing, and deploying analytics models enabling predictive maintenance and intelligent insights will be a key focus. It will also be crucial to productionize analytical solutions, ensuring robustness, scalability, and efficiency in a cloud environment. Leveraging IoT data for driving improvements in predictive analytics and intelligent insights will also be an essential aspect of your role. Implementing CI/CD pipelines and MLOps best practices for model deployment and monitoring will be vital for success. Key Responsibilities: - Provide technical leadership to data scientists and data engineers - Foster skill development and best practices within the team - Collaborate with business, product, and engineering teams to design scalable AI/ML solutions - Develop, optimize, and deploy analytics models for predictive maintenance and intelligent insights - Ensure robustness, scalability, and efficiency of analytical solutions in a cloud environment - Leverage IoT data for driving improvements in predictive analytics and intelligent insights - Implement CI/CD pipelines and MLOps best practices for model deployment and monitoring Qualifications Required: - Bachelors or equivalent experience in Computer Science, Data Science, Machine Learning, or a related quantitative field - Proven hands-on experience in developing, deploying, and managing ML solutions in production - Background in statistical modeling, deep learning, and machine learning techniques - Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, Scikit-Learn - Experience working with cloud platforms (AWS, Azure, or GCP) and scalable architectures - Experience in software engineering practices, including CI/CD, DevOps, containerization (Docker, Kubernetes), and infrastructure as code - Proven track record of leading and managing data teams, including data scientists and data engineers - Strong communication and collaboration skills - Can-do attitude with a growth mindset,