Jr R&D Data Scientist

EOS-posted 3 months ago
Entry Level
501-1,000 employees
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Eos Energy Enterprises, Inc. is accelerating the shift to American energy independence with positively ingenious solutions that transform how the world stores power. Our breakthrough Znyth™ aqueous zinc battery was designed to overcome the limitations of conventional lithium-ion technology. It is safe, scalable, efficient, sustainable, manufactured in the U.S., and the core of our innovative systems that today provides utility, industrial, and commercial customers with a proven, reliable energy storage alternative for 3 to 12-hour applications. Eos was founded in 2008 and is headquartered in Edison, New Jersey. For more information about Eos (NASDAQ: EOSE), visit eose.com.

  • Conduct analyses, build models, and develop tools to support Eos in managing and optimizing the operation of its growing fleet of installed energy storage systems, using both traditional analysis and machine learning techniques.
  • Carry out analyses as needed to support other teams such as field service, operations, product testing, & manufacturing.
  • Support development of infrastructure to automate and integrate any developed models & algorithms and their derived learnings.
  • Communicate findings to other stakeholders in the form of presentations, reports, browser-hosted dashboards to disseminate findings.
  • Create derived documentation or training materials as needed.
  • Strong general-purpose scripting skills.
  • Background in mathematics and statistics.
  • Experience developing machine learning models.
  • Python strongly preferred (numerical/data packages such as Numpy, Pandas, Scipy, etc. beneficial).
  • Some knowledge of SQL databases.
  • Strong technical writing and reporting skills.
  • Basic familiarity with Linux.
  • Ability to work independently and well within a team.
  • Experience with timeseries telemetry data or similar preferred.
  • Experience with operational data from deployed field systems, IOT systems, etc. preferred.
  • Experience modelling physical systems, control systems, etc. preferred.
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