Applied Science Lead, Data Opportunities

Gridware exists to enhance and protect the mother of all networks: the electrical grid. The grid touches everything, and when it grinds to a halt, the consequences can be dire: wildfires burn, land is destroyed, property is damaged, progress stops, and lives are lost.

Our team engineers an advanced sensing system to continuously analyze both the electrical and mechanical behavior of grid assets. Utilizing high-precision sensor arrays, the system identifies and allows preemptive mitigation of faults. The technology has been proven with utilities to bolster safety, reliability, and reduce customer outage durations. The demand for power will only increase. We protect the grid of today while we build the grid of tomorrow.

Gridware is privately held and backed by the best climate-tech and Silicon Valley investors. We are headquartered in the Bay Area in northern California.

Gridware is seeking a creative and fast-paced applied science lead to join our growing team. At Gridware, you will be responsible for evaluating and developing models utilizing a variety of time series, categorical, and graph data from the physical world. The ideal candidate will possess deep knowledge of machine learning, data science, and product development.

Responsibilities 

This is what you’ll be doing:

• Execute exploratory data analysis and proof-of-feasibility/concept for new data products using new or existing data
• Validate and warehouse highly disparate data sources for prototyping applications
• Conduct literature reviews and research on large-scale sensor fusion and technical model validation
• Work closely with cross-functional teams, including research engineers, sales, and product

Required Skills

To move forward in the application process you should have all of these:

• 1+ years of leading technical teams at the intersection of data science and physical science
• 2+ years of professional experience with data product development and nascent applications
• 4+ years of professional experience with production machine learning models
• 4+ years of research experience
• Strong fundamentals in physical modeling and data science

Most relevant to people with degrees in:

• Computer Science
• Data Science
• Statistics/Mathematics
• Physics/Bio/Chem