Electric Power Research Institute→
Staff - Level I , Data Scientist at Electric… · Charlotte
Skills
Job Description
Job Title:
Staff - Level I , Data ScientistLocation:
Charlotte, NCJob Summary and Description:
Job Summary and Description:
EPRI and the broader energy industry are actively conducting research and development and investing in advanced technologies to ensure the delivery of safe, affordable, and reliable power generation. Central to addressing the industry’s most complex and evolving challenges are data analytics and artificial intelligence, which serve as foundational tools for informed, data driven decision making. The development and application of analytics and AI are embedded across EPRI’s research portfolio and deliver tangible value to our members, the energy sector, and the public at large. Within power generation, these capabilities are increasingly focused on enhancing asset reliability, operational performance, and system resilience. In alignment with EPRI’s data driven vision, analytics and AI are also leveraged to strengthen the security of our cyber and digital infrastructure. To support these strategic priorities and our commitment to delivering impactful solutions to the industry, EPRI is seeking data analysts and data scientists to join a collaborative, cross functional team.
EPRI’s Generation Sector and the Monitoring and Advanced Data Analytics Program are seeking a Staff Data Scientist (Level 1 to Level 3) with the following general responsibilities and expectations:
• Collaborate closely with EPRI research staff across multiple programs and sectors to support data science, analytics, and AI focused research initiatives under moderate supervision.
• Assist with the acquisition, cleaning, analysis, and visualization of both structured and unstructured datasets to support high quality research deliverables.
• Support the design, development, testing, and documentation of data driven tools, analytical models, and AI enabled prototypes.
• Contribute to applied research activities by conducting exploratory data analyses, validating analytical assumptions, and synthesizing results into clear, actionable insights.
• Help document existing analytical methods, datasets, and tools to promote knowledge sharing, transparency, and reproducibility across teams.
• Participate in research projects emphasizing high technology readiness level (TRL) applications that can be digitally transferred and deployed for EPRI member use.
• Operate effectively within a matrixed organizational environment, supporting multiple projects concurrently based on evolving priorities, skills, and professional development objectives.
As a Staff Data Scientist, the ideal candidate will be integral to building robust data preprocessing pipelines and machine learning workflows that improve condition monitoring, anomaly detection, and prognostics for power generation performance and reliability. The role focuses primarily on sensor and operational data and requires strong skills in Python, SQL, data quality engineering, and end to end ML pipeline development, along with clear technical communication and collaboration across engineering and analytics teams.
Data Engineering and Preprocessing:
• Familiarity with large-scale data analysis (e.g., working with hundreds of thousands to millions of records).
• Interest in conducting exploratory data analysis, data cleaning, and validation on real-world, imperfect datasets.
• Build and maintain Python based preprocessing and ETL workflows for sensor and operational datasets.
• Standardize inconsistent CSV and Excel sources into analytics ready formats and design schemas for reliable storage and reuse.
• Implement automated data profiling, data quality checks, anomaly and outlier detection, and label quality assessments.
• Create reusable components for feature engineering and dataset preparation that support efficient experimentation and model iteration.
Monitoring and Diagnostic Analytics:
• Develop models and rule guided analytics for equipment anomaly detection, degradation mechanism identification, and predictive maintenance.
• Incorporate subject matter expertise and physically meaningful signals into feature engineering and model interpretation.
• Deliver live demonstrations and concise narratives that explain methodology, assumptions and operational value to internal and external stakeholders.
• Interest or exposure to cybersecurity, OT data mining, anomaly detection, or system vulnerability analysis.
Machine Learning and Workflow Development:
• Build complete ML pipelines for supervised and unsupervised learning including feature generation, training, evaluation and comparison.
• Benchmark alternative algorithms and document results to support selection of reliable, maintainable solutions.
• Package pipelines for reproducibility, reuse and rapid iteration across datasets and equipment types.
Data Systems and Collaboration:
• Design and query relational databases to organize heterogeneous operational and historical data.
• Collaborate with cross functional teams to scope requirements, validate results, and integrate analytics into monitoring workflows.
• Produce clear documentation and reports suitable for peer review and stakeholder consumption.
Basic Qualifications:
• Bachelor’s or master’s degree in computer science, Data Science, Electrical Engineering, Mechanical Engineering or a related field.
• Proficiency in Python for data processing and machine learning including NumPy, Pandas and Scikit learn.
• Hands on experience creating preprocessing pipelines, cleaning and transforming noisy datasets and preparing features for modeling.
• Strong understanding of algorithms, data structures and software engineering practices for analytical codebases.
• Excellent written and verbal communication skills demonstrated through technical reports and presentations.
• Ability to work independently, take ownership of workstreams and collaborate effectively with engineering SMEs and other stakeholders.
Preferred Qualifications
Technical Experience:
• Experience with anomaly detection methods for noisy tabular or time series data and end to end ML workflow automation.
• Experience with SQL database design, schema normalization and complex querying for analytics support.
• Familiarity with model reliability techniques and data centric AI practices including tools such as CleanLab.
• Exposure to computer vision concepts and OpenCV from coursework or projects.
• Familiarity with cloud concepts and workflows from academic or project experience (e.g., AWS).
• Experience applying ML and statistical reasoning to power plant or industrial equipment data is preferred but not required.
Professional Attributes:
• Demonstrated initiative building tools and frameworks from scratch to meet unique data constraints.
• Ability to translate ambiguous requirements into structured tasks and deliver high quality results on time.
Tools and Technologies
• Python, NumPy, Pandas, Scikit learn
• SQL and relational databases
• Optional familiarity with CleanLab and OpenCV
• Experience with Excel for data reshaping and validation in support of analytics workflows
• Comfortable using AI assisted productivity tools when appropriate
Work Environment
Join a collaborative team combining engineering and data science to modernize monitoring and diagnostic practices used by utilities and industry partners. You will contribute to high impact projects, publish rigorously documented methods and support demonstrations that communicate operational value.
Travel Requirements
Up to 20 percent travel for facility visits, workshops and partner meetings as needed.
Key Responsibilities and Requirements:
- Conducts technical searches and analyzes technical information in support of the project team.
- May conduct research at the direction of the project manager.
- Work is performed under general supervision.
Education: Bachelor’s Degree in a technical field or equivalent experience required.