Predictive battery management software for electrified fleets.
We are looking for a self-starting, highly motivated data scientist or researcher who is not afraid of getting into the details of battery modeling problems. This person will help us refine and improve algorithms for internal state estimation of batteries and predictive algorithms for performance. Their skills should include method and algorithm development, with a strong emphasis on drawing conclusions from experimental data and being able to communicate those conclusions to a technical, non-expert audience. In particular, this person should have a strong grasp of data analysis methods, model optimization approaches and be able to independently evaluate and determine the appropriate tools for measuring error, variance, sensitivity, etc.
Additionally, this person will be partially responsible for designing data curation and analysis as part of the modeling and training workflow. Experience with visualization and data pipelines is a plus.
The candidate for this position should have at least one of the following target experience levels:
In order to keep climate change to 1.5°C, we’ll need 30% of global GDP (all of energy generation and transportation) to run on batteries by 2035.
Zitara Technologies (YCombinator S20) builds predictive battery management software for transportation and energy customers with large deployments. Their customers operate >$100M deployments of batteries in satellites, EVs, and renewable energy storage installations.