Intern - Power Systems Time-Series Analytics

We Strive to Drive Power Innovation Through Data

The nature of the grid is in flux. We on the Engineering Analytics & Modeling team see this challenge and aim to rise to the occasion. Our team mandate is simple: we aim to bridge the gap between real-world system data and innovative capabilities that provide consumable business intelligence and help steer the future of how we monitor, plan, and manage the grid.

Our team is responsible for the capture, utilization, and application of time-series data, including synchrophasors, SCADA, point-on-wave measurements, load and generation data, and other supporting data. Our work is to steward this data and ensure its analysis and outputs influence our business and the industry as they adapt to meet the demands of a shifting energy landscape. We seek the curious, the self-motivated, and those driven by the pursuit of change.

Job Summary: We Seek Joint Expertise in Power Systems, Time-Series Analysis, and Data Analytics

Our team comprises a critical part of the Strategic Initiatives team within the Electric Transmission organization. We focus on addressing a broad spectrum of transformational and innovative projects by solving problems through real-world measurements, data analytics, forecasting, statistical analysis, signal processing, and custom-developed modeling and software capabilities.

The consistent thread across our work is the use of time-series measurement data to identify trends, understand system behavior, generate insights, and support better planning and operating decisions. A successful candidate will be able to:

• Navigate and interrogate an array of available power system and supporting data sources.
• Analyze time-series data across different time horizons and measurement resolutions.
• Identify trends, patterns, relationships, anomalies, and other insights within historical data.
• Support the development of analytical tools and capabilities aimed at observing, diagnosing, forecasting, and surfacing system behavior.
• Explore new applications of time-series analysis, statistical modeling, forecasting, signal analysis, or related methods within power systems.
• Apply statistical analysis, machine learning, and econometric methods to the development and evaluation of forecasting models.
• Explore longer-horizon, scenario-based, and probabilistic forecasting techniques to better characterize uncertainty and a range of potential future outcomes.
• Develop transparent, explainable, and presentable analyses or models that can be understood and used by technical and business stakeholders.
• Analyze and compare model or forecast outputs with actual system results, explain variances, and recommend potential improvements.
• Communicate findings through clear reports, visualizations, and presentations.

Candidates are not expected to have experience across every area of the team’s work. Experience or academic research in either of the following areas would be particularly valuable:

• Electric load, generation, renewable energy, or other forms of power system forecasting.
• Synchrophasor, phasor measurement unit, waveform, or point-on-wave data analysis, including the analysis of dynamic system behavior.

These backgrounds are appreciated but are not mandatory. Candidates with broader experience in time-series analysis, statistical modeling, signal processing, measurement analysis, or data-intensive engineering applications are encouraged to apply.

Required Knowledge, Skills, Abilities & Experience

The knowledge, skills, abilities, and experiences required for entry into this job include the following:

• Engineering and power systems: Strong engineering or quantitative background, preferably in electric power systems. Experience with power system measurements and operational data, such as load, generation, SCADA, synchrophasor, or waveform data, is highly valued.
• Time-series, mathematics, and statistics: Strong grasp of statistical approaches for time-series data, with the mathematical intuition to understand when and why methods are appropriate rather than applying them as black-box tools. Experience with statistical modeling, machine learning, forecasting, signal processing, econometrics, or probabilistic methods is highly valued.
• Analytical programming and data science: Strong analytical programming skills, preferably in Python, with experience using scientific computing, statistical analysis, and machine-learning libraries to develop transparent, explainable, and reproducible analyses.
• Critical thinking: Our team requires an ability to think abstractly, pursue unknown unknowns, and persevere when working through new and challenging problems. We seek candidates who are willing to explore alternative methods while maintaining analytical rigor.
• Autonomy and self-starting: We are developing new industry capabilities and seek candidates who can operate independently while remaining aligned with the joint mission of the team.
• Communication skills: The candidate should be able to translate complex analytical results into clear reports, visualizations, and presentations for technical and nontechnical audiences.
• Professional ethics: The candidate should demonstrate excellent professional ethics and responsible handling of operational and business data.

Education Requirements

Candidates must be working toward a graduate level degree in engineering, mathematics, statistics, computer science, data science, or a related technical discipline.

Electrical engineering with a focus on power systems is strongly preferred but is not required when the candidate demonstrates relevant experience with time-series data and analytical methods.

• Candidates must be graduating in Spring 2028 or later.

• Candidates must have a 3.5 GPA or above.

Test Description

No Testing Required

Export Control

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