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Power Factors

Cloud-based asset performance management for renewables

Senior Data Scientist - Battery Energy Storage Systems

Applied Machine LearningPosted on Nov 12
Full-timeHouston, TX, USANo salary listed

About the Job

Requirements

  • At least 5 years of experience in the energy industry, with a strong emphasis on utility-scale battery energy storage systems.
  • Deep, hands-on understanding of battery energy storage system operation, performance, state-of-charge and state-of-health estimation, thermal management, degradation mechanisms, and lifecycle management.
  • Demonstrated experience working with large-scale operational battery energy storage datasets, including battery management system telemetry, supervisory control and data acquisition data, event logs, cycle data, and high-frequency time series.
  • Proven experience developing analytical solutions used by asset owners, operators, integrators, or original equipment manufacturer-adjacent organizations to support operational and strategic decision-making.
  • Proficiency in Python and modern data science libraries such as pandas, NumPy, scikit-learn, TensorFlow, or PyTorch.
  • Strong grounding in statistics, machine learning, and applied artificial intelligence, with demonstrated real-world deployment experience.
  • Experience with time-series analysis, signal processing, anomaly detection, forecasting, and predictive modeling in industrial or electrochemical contexts.
  • Experience with version control using Git and collaborative software development practices.
  • Ability to clearly communicate complex analytical concepts, assumptions, and results to technical and non-technical stakeholders.
  • Experience working closely with software engineering teams in production environments, including application programming interfaces, services, and deployment pipelines.
  • Proven ability to translate operational challenges and customer needs into analytically sound and implementable solutions.
  • High degree of autonomy and ownership in complex, technically ambiguous problem spaces.
  • Scientific rigor, intellectual curiosity, and attention to detail.
  • Comfort working on long-horizon, technically demanding problems where analytical quality and robustness are critical.

Responsibilities

  • Identify, evaluate, and prioritize high-impact advanced analytics opportunities focused on battery energy storage system performance, health, and revenue optimization.
  • Lead the development of advanced data science solutions from concept through production deployment.
  • Use the company’s large-scale operational battery energy storage database to build analytically rigorous, scalable models used across global storage fleets.
  • Collaborate with product and engineering teams to ensure analytical solutions are robust, interpretable, and operationally actionable.
  • Apply deep battery energy storage domain knowledge to define analytically sound problem formulations, feature engineering strategies, and model validation approaches.
  • Engage directly with technically sophisticated customers to explain analytical methods, assumptions, and results with clarity and precision.
  • Serve as an internal battery energy storage subject matter expert, supporting product, sales, and customer success teams with domain guidance.
  • Stay current with advances in battery science, electrochemical modeling, grid storage economics, and machine learning for energy systems.
  • Propose and lead experiments to improve model quality, expand analytical coverage, and deliver new product capabilities.
  • Contribute to the external scientific and technical community through publications, conference presentations, or open-source contributions where appropriate.

Desired Qualifications

  • A Ph.D. or master's degree in electrochemical engineering, physics, applied mathematics, statistics, computer science, or a related quantitative field.
  • Specialized experience in battery energy storage analytics, including electrochemical model-based or data-driven state-of-health and remaining-useful-life estimation; degradation and calendar/cycle life modeling; dispatch optimization and revenue stack modeling; augmentation strategy and capacity fade forecasting; or thermal runaway risk modeling or fault detection.
  • Experience with real-time or near-real-time analytics pipelines.
  • Hands-on experience with data engineering concepts, cloud platforms such as AWS, Azure, or GCP, and scalable data pipelines.
  • Knowledge of power markets, capacity markets, ancillary services, power purchase agreements, or grid interconnection as they relate to battery energy storage assets.
  • Experience with machine learning operations practices and large-scale model deployment.
  • Scientific publications, patents, or technical conference presentations in battery analytics, applied machine learning, or related fields.

About the Company

Power Factors

Cloud-based asset performance management for renewables

Power Factors provides a cloud-based SaaS platform for the renewable energy sector that helps asset owners, operators, and independent power producers optimize large portfolios of wind, solar, and energy storage assets. Its Unity energy management suite unifies monitoring, analytics, field service management, and commercial asset management into one platform that collects data from many sources to support remote asset management. The platform uses SCADA data, AI-based predictive analytics, and automated workflows built on OSIsoft PI and Microsoft Azure to improve revenue and reduce manual tasks. It differentiates itself by merging multiple capabilities from a series of acquisitions into a single integrated system at scale, and its goal is to maximize financial and physical performance while expanding global adoption.

Company size
501-1,000
Company stage
Not listed
Total funding
Not listed
Headquarters
Larkspur, CA
Founded
2012

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