Gridware has developed an innovative hardware product that plays a crucial role in safeguarding communities from potential disasters related to electricity distribution, such as prolonged power outages, wildfires, and dangerous incidents involving the public. By utilizing advanced sensors, our technology promptly detects and locates grid disturbances, enabling rapid response to prevent or mitigate their impact. Join us in our mission to enhance safety and reliability for communities worldwide, protecting against catastrophic events before they occur.

We’re currently looking for an experienced Data Scientist to help us build tools and systems to detect electrical grid faults with data collected by our fleet of devices around the clock.

Why do we need you?

At a pivotal moment in our growth, we've successfully deployed our innovative power grid monitoring devices across several regions, marking a significant milestone in our mission to enhance the safety and efficiency of power distribution networks worldwide. We need build intelligent systems to handle efficiently handle a rapidly increasing fleet of devices while ensuring that each potential event is fully investigated.

As an early team member of our operations team you will play a critical role in building our next generation monitoring system and developing novel detectors to identify previously unseen faults in the grid. You will provide scientific support to growing number of operational hubs in various countries as we expand beyond the United States. You will help create a integrated technical system which prevents potential disasters, ensures fleet reliability, and safeguards communities

Responsibilities:

1. Develop an advanced operations alerting system to efficiently respond to real time events on the electrical grid.
2. Leverage intelligent processing of high-frequency data across multiple sensor modalities to generate informative features for both human and machine learning models.
3. Advance research and development efforts for characterization and detection of faults on the electrical grid.
4. Expand monitoring capabilities to identify long-term risks to the electrical grid and explore new markets for safety and reliability improvement.

Ideal Candidate:

• Has an advanced degree in engineering or a physical science.
• Has a track record of delivering multiple projects using diverse and distributed sensor data to classify or detect natural phenomena.
• Has experience in preparing real-time data and tools for operations centers, such as security monitoring, weather, pipeline, wind turbine, or utility management.
• Proven experience in presenting data to diverse audiences through dashboards and user-facing software tools.

Minimal Qualifications:

• 3 years of experience in analyzing and interpreting sensor or instrumentation data within a scientific or engineering context.
• Demonstrated ownership and delivery of at least two scientific projects involving event detection or classification based on sensor data.
• Working knowledge of signal processing and time-series analysis.
• Proficiency with Python, Git, and SQL.
• Familiarity with C++ and Spark.
• Ability to work in a Linux environment.

Benefits

Our employees enjoy working in a mission-driven environment to create a future with safe and reliable energy. Beyond that, we offer competitive benefits that help them to thrive and grow. These benefits include:

- Health, Dental and Vision insurance, free parking and a commuter allowance

- 401K

- Stock option plan

Gridware is an equal opportunity employer. We want applicants of diverse backgrounds and hire without regard to color, gender, religion, national origin, citizenship, disability, age, sexual orientation, or any other characteristic protected by law.

Compensation

Gridware determines pay ranges by utilizing a wide variety of open and closed source compensation datasets. Base salary offered to candidates are typically determined by a range of factors including experience, credentials, job-related skill level, scope of responsibilities, and role level.