Senior Data Scientist; Power Systems

About Phaidra

Phaidra is building the future of industrial automation.

The world today is filled with static, monolithic infrastructure. Factories, power plants, buildings, etc. operate the same they've operated for decades — because the controls programming is hard‑coded. Thousands of lines of rules and heuristics that define how the machines interact with each other. The result of all this hard‑coding is that facilities are frozen in time, unable to adapt to their environment while their performance slowly degrades.

Phaidra creates AI‑powered control systems for the industrial sector, enabling industrial facilities to automatically learn and improve over time. Specifically:

• We use reinforcement learning algorithms to provide this intelligence, converting raw sensor data into high‑value actions and decisions.
• We focus on industrial applications, which tend to be well‑sensorized with measurable KPIs —perfect for reinforcement learning.
• We enable domain experts (our users) to configure the AI control systems (i.e. agents) without writing code. They define what they want their AI agents to do, and we do it for them.

Our team has a track record of applying AI to some of the toughest problems. From achieving superhuman performance with Deep Mind's Alpha Go, to reducing the energy required to cool Google’s Data Centers by 40%, we deeply understand AI and how to apply it in production for massive impact.

Phaidra’s ability to achieve its mission is determined by our ability to work together — as defined by our core values:
Transparency
, Collaboration
, Operational Excellence
, Ownership
, and
Empathy. We seek individuals who embody these values, as they are instrumental in ensuring our team consistently delivers excellence and fosters an engaging and supportive culture.

Phaidra is based in the USA, but we are 100% remote with no physical office. We hire employees internationally with the help of our partner, Oyster

HR. Our team is currently located throughout the USA, Canada, UK, Italy, Sweden, Spain, Portugal, the Netherlands, Singapore, Australia, and India.

Please submit your resume/CV below. You may also submit a cover letter explaining what your ideal position is and how your skills would fit with the team!

Please note: Due to the high volume of applications, there may be a delay in response from our hiring team. However, Phaidra is committed to ensuring every applicant receives a response, regardless of the outcome. We sincerely appreciate your interest in joining Phaidra and thank you for taking the time to apply.

• Develop electrical system ontology for data centers which will be used to organize customer’s system data in a way where it can be used by AI & LLMs for customer facing products and services.
• Create and utilize tools to continuously monitor system telemetry from sensors, smart meters, and facility management systems to detect early signs of equipment degradation to help prevent service disruptions.
• Develop and deploy advanced anomaly detection models using machine learning and statistical methods to identify irregularities in power usage, voltage stability, cooling performance, UPS/battery behavior and more.
• Design automated alerting frameworks that trigger real‑time notifications for operational teams when abnormal conditions are detected (e.g., power spikes, cooling failures, or unexpected load distributions).
• Perform root cause analysis on anomalies and power incidents, correlating data across multiple systems (electrical, mechanical, environmental) to improve reliability and uptime.
• Collaborate with customer operations teams to translate anomaly insights into actionable maintenance and optimization strategies, reducing unplanned downtime and improving PUE (Power Usage Effectiveness).
• Grow the knowledge base about data center electrical systems within Phaidra by openly sharing your knowledge and experience.

• In the United States, we accept applicants located in the following states: California, Colorado, Connecticut, Georgia, Florida, Indiana, Maryland, Minnesota, Missouri,…