Our customer is automating clean energy deployment within cities across the country.
To start, they’re making getting a fully-financed clean energy system for a commercial building as easy as applying for a credit card. The scope of the opportunity is massive: out of 6 million commercial buildings in the US, fewer than 4% have solar energy generation.
They make clean energy widely accessible by removing the complexity of an underlying marketplace of installers, financiers and confusing electrical jargon. To do this the technical challenge is to parse massive datasets, deploy advanced models behind the scenes, pre-package financing, all so that we can deliver a simple, delightful, and actionable offer to our customers.
The small team of scientists, engineers, designers and entrepreneurs committed to powering our world with clean energy. While early in their journey, they are live with customers, funded by exceptional investors, and looking to bring on a core team member who can help expand their impact.
- Building predictive models of energy usage and optimal technology suitability for buildings globally, combined from a heterogeneous mix of time-series, geospatial, and imagery data
- Build novel financing and credit models, in collaboration with finance experts on our team
- Lead, grow, and foster high-performance teams of in-house data scientists and ML experts from around the world
- Collaborate with external expert advisors at universities and US national labs
You love cracking the code. You build. You gravitate towards making a global impact. While you might enjoy exploring the frontiers of data science and ML papers, you are ruthlessly pragmatic.
You are comfortable manipulating time-series, geospatial, and imagery data alike (though you may have your personal preference!) You have a keen sense of when to employ disposable hacks, and are ready to architect scalable data pipelines that serve production software.