Company Profile:

At KONE, our mission is to improve the flow of urban life. As a global leader in the elevator and escalator industry, KONE provides elevators, escalators, and automatic building doors, as well as solutions for maintenance and modernization, which add value to the life cycle of any building. Through more effective People Flow®, we make people’s journeys safe, convenient and reliable, in taller, smarter buildings. Together with our partners and customers around the world, we help cities to become better places to live in. In 2016, KONE had annual net sales of EUR 8.8 billion, and at the end of the year over 52,000 employees. KONE class B shares are listed on the Nasdaq Helsinki Ltd. in Finland.

Job description:

We are looking for a Data & Analytics Chapter Lead to join our team—a hands-on expert who can develop and deploy analytics solutions at scale while also mentoring and guiding our data science team. Our global analytics solution for predictive maintenance relies heavily on cloud development and strong software engineering skills, making this role crucial in driving innovation and operational excellence.

1. In this role, you will:

Provide technical leadership to data scientists and data engineers, fostering skill development and best practices

Work closely with business, product, and engineering teams to identify challenges and design scalable AI/ML solutions

Develop, optimize, and deploy analytics models that enable predictive maintenance and intelligent insights

Productionize analytical solutions, ensuring robustness, scalability, and efficiency in a cloud environment

Leverage IoT data to drive improvements in predictive analytics and intelligent insights.

Implement CI/CD pipelines and MLOps best practices for model deployment and monitoring

2. We welcome candidates with:

MSc/PhD 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