Landis+Gyr is a leading global provider of integrated energy management solutions. We measure and analyze energy utilization to generate empowering analytics for smart grid and infrastructure management, enabling utilities and consumers to reduce energy consumption. Our innovative and proven portfolio of software, services and intelligent sensor technology is a key driver to decarbonize the grid. Having avoided more than 9 million tons of CO2 in FY 2021 and committed to achieve carbon neutrality by 2030, Landis+Gyr manages energy better – since 1896. 
With sales of USD 1.5 billion in FY 2021, Landis+Gyr employs around 6,500 talented people across five continents.

In partnership with multiple stakeholders, you will focus on developing and delivering leading edge analytics solutions using Google Cloud as well as engineering the ML solutions required to get a fully operational product for our utility customers. As a member of our engineering practice, you will be part of a small team of data and ML engineers which is working with a DevOps culture within our product team.

• Design and implement modern, scalable ML solutions using a range of new and emerging technologies from Google Cloud
• Define and support the research and analytical process to deliver business insights
• Responsible for advanced statistical and machine learning modeling
• Develop data driven analytical use cases in cooperation with other team members - Build Machine Learning models that can be used for asset health and grid operations
• Work with Agile and DevOps techniques and implementation approaches in the delivery
• Liaise and be part of our Google Cloud practice, contributing in the knowledge exchange learning programme of the platform

Expectations for Skills & Experience:

• Experience in Google Cloud will be a plus, preferably using BigQuery or similar data warehouses as well as BigQuery ML and Vertex AI to run ML models
• Programming experience with either SAS/SQL/R or Python
• E2E Data Engineering and Lifecycle (including non-functional requirements and operations management)
• Experience with advanced statistical and machine learning techniques
• Ability to initiate and drive analytics projects from inception to delivery, knowledge of ML Ops to deliver ML operational solutions
• Experience working with CI/CD pipelines and test automation will be a plus
• Experience with utility time series data will be a plus

If you have the drive and enthusiasm to accept this challenge and to work in a very dynamic, cross functional and international environment, we are looking forward to meeting you. Click apply now.