Ento Labs is building an AI-first product to make energy optimization in buildings much faster and cheaper as no additional hardware or onsite visits are required.
Energy efficiency is a key driver in most net-zero scenarios, but the reports don't explain how building owners are supposed to understand all of the different solutions let alone prioritize, implement and verify them. To solve this, we (ML) model energy consumption from low-resolution smart meter data and provide a prioritized list of recommended actions to take across a building portfolio. With each action taken, the system improves and documents savings for the building owner.
To take advantage of data network effects, our system is designed to scale horizontally for any number of buildings. We're growing rapidly, now monitoring ~20,000 meters and have helped save ~500 tons of annual CO2 emissions in less than a year since launch. Our company is basically a machine that effectively converts data and code into emission reductions by interacting with the existing energy optimization environment (technicians, vendors etc).
In this role you'll join a small but fast growing team of data scientist, software engineers and energy experts to architect and build out the product in close connection with the rest of the company, customers and partners. Our stack is AWS, Python + Django, Postgres, Redis.
More info here: https://thehub.io/startups/ento-labs