.png){kind=logo}
SolvingZero
We use individual smart meter data to help homeowners buy batteries. We can access the smart meter data for any home in the NEM (any state or territory other than WA and NT) via a government-led API. We use the data to understand the home's unique consumption patterns and to simulate battery performance.
Ideally, we're looking for someone who would be able to meet on a weekly basis at our Melbourne office.
About the team
A solo founder-led team with currently two overseas developers based in New York and Ukraine. The founder has worked in startups in both Melbourne and San Francisco, from pre-seed to nine-figure-valued companies. Scaling operations, marketing, engineering and other departments.
We're hoping to craft a culture of mutual respect, genuine effort, and rewarding work that we all enjoy waking up for.
About the job
Who we are:
We help homeowners buy home batteries. We do this by fetching their energy data via API from their smart meter and calculating the optimal battery size based on their consumption patterns.
Where we are at:
We have recently won a grant for $210k to work alongside a university and integrate some of their research into our platform. Our backend is built on Python and Clojure, our frontend is all Typescript, we rely heavily on GCP and use Timescale as our time series DB. There are plans to migrate purely to TS and Python.
What we are looking for:
We are 100% focused on sustainable revenue, we need someone who can go deep into the build, measure, and learn loop. Focusing on finding a path to sustainable commercialisation will drive the core of our work and will be a big focus. We have some cash in the bank via the grant, and now have room to test a suite of features and revenue models to see what does and does not work.
Goals: There is a lot of low-hanging fruit around cleaning our processes, architecture, data analysis, functionality, etc. We need to prioritise the drivers of revenue and choose those first and foremost. This role will play a huge part in guiding that strategy and diving into the work, potentially working with a UX specialist and the founder to bring your work to users quickly. There is also scope to potentially manage one overseas contractor.
Requirements
Hard skills: Sound grasp of TypeScript and Python. An understanding of basic data science principles, in particular classical ML models (regression, random forests, gradient boosting) applied to time series problems. You should be comfortable with reasoning about model accuracy on real, messy data. Experience with a numerical Python stack (NumPy, scikit learn, SciPy, and ideally Polars) is ideal. Bonus points for any exposure to energy data, physical simulation, or optimisation like linear programming.
Soft skills: Be a strong communicator who can efficiently organise your thoughts, work and proposals to your teammates. Respectful honesty in communication is highly regarded.
