LG Energy Solution Michigan, Inc.
Data Scientist
Company Overview
LG Energy Solution Vertech, Inc. (LGES Vertech) is a full-service energy storage system supplier and integrator. Using our core strengths of expert service to our customers, unparalleled safety, and excellence in manufacturing, we bring standardized, fully integrated energy storage systems to a rapidly growing worldwide market. Our systems address our customers' needs to reduce capital equipment and installation costs while enhancing system-level performance and reliability using automated monitoring systems and analytics across the battery, power conditioning, and auxiliary systems. Our AEROS energy operating system is the engine of innovation to provide advanced control functions, allowing our customers to maximize the value of their energy storage assets. Our service capabilities include advanced monitoring and analytics, scheduled maintenance, augmentation, and auxiliary system upgrades. The combination of excellence in battery technology and production, coupled with nearly two decades of energy storage integration, makes LGES Vertech a supplier and integrator in the power and energy markets.
LGES Vertech empowers and expects its team members to assume responsibility and make good decisions while maintaining a team environment that fosters collaboration and innovation. Our diverse and growing team enjoys competitive salaries, generous benefits, and flexible working hours.
For more information about LGESVT, please visit www.lgensol-vt.com.
Position Overview
LGES-Vertech is seeking a highly motivated Data Scientist to join our Data Science and Predictive Analytics team, where you'll be a key contributor in shaping intelligent, data-driven solutions for Battery Energy Storage Systems (BESS). You will play a key role in developing and deploying advanced machine learning solutions that address critical challenges in system performance, anomaly detection, diagnostics, and predictive maintenance for energy storage systems.
This role offers the opportunity to research and develop new algorithms, build scalable products, and directly influence how data science enhances the safety, availability, and commercial value of BESS systems.
Key Responsibilities Design and develop advanced machine learning solutions to detect anomalies, diagnose root causes, and forecast potential failures in Battery Energy Storage Systems (BESS). Take ownership of projects end-to-end from problem formulation and data exploration to model development, validation, and deployment in production environments. Build and maintain custom diagnostic and prognostic models that go beyond event detection to generate actionable insights for reliability, safety, and performance optimization. Collaborate closely with domain experts, data engineers, and DevOps to ensure models are integrated into scalable cloud-based pipelines. Lead research and prototyping of new methods, including unsupervised learning, statistical modeling, and signal processing techniques, to handle complex time-series and event data. Page 2 of 2 Analyze large-scale, imperfect, and noisy datasets from deployed field systems to uncover hidden patterns, trends, and failure modes. Contribute to and maintain a robust, modular codebase with clear documentation and versioning practices, following software engineering best practices. Support development of tools and visualizations to communicate diagnostics and predictions to engineering, operations, and customer teams. Job Requirements
Required Qualifications:
Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related quantitative discipline. 3-6 years of hands-on experience applying machine learning in signal processing, manufacturing, energy or other related industry. Demonstrated ability to independently develop and deploy custom machine learning algorithms, not just using off-the-shelf models. Proven expertise in anomaly detection, fault diagnostics, and system-level prognostics using time series and high-frequency data. Experience working with complex, noisy datasets from physical systems, with a strong emphasis on signal interpretation and pattern extraction. Strong Python development skills, including best practices in modular, scalable, and testable codebases. Deep understanding of both supervised and unsupervised learning methods; able to apply advanced models such as isolation forests, autoencoders, Bayesian inference, or graph-based methods. Familiarity with machine learning frameworks such as PyTorch, TensorFlow, or Scikit-learn. Comfortable working in a Linux environment and with Git-based workflows. Must be legally authorized to work in the U.S. without employer sponsorship.
Preferred: M.S or Ph.D. in Computer Science, Engineering, Physics, or a related field. Familiarity with cloud platforms: AWS, Azure, GCP, Databricks, or Snowflake. Familiarity with MLOps tools for model deployment, monitoring, and lifecycle management. Familiarity with CI/CD pipelines and integrating ML models into DevOps workflows. Familiarity with data engineering architecture and performing trade-off analyses using various cloud services. User-level experience with real-time monitoring and optimization of deployed pipelines. Experience in battery management systems, energy storage diagnostics, or predictive maintenance in the energy domain
Company Overview
LG Energy Solution Vertech, Inc. (LGES Vertech) is a full-service energy storage system supplier and integrator. Using our core strengths of expert service to our customers, unparalleled safety, and excellence in manufacturing, we bring standardized, fully integrated energy storage systems to a rapidly growing worldwide market. Our systems address our customers' needs to reduce capital equipment and installation costs while enhancing system-level performance and reliability using automated monitoring systems and analytics across the battery, power conditioning, and auxiliary systems. Our AEROS energy operating system is the engine of innovation to provide advanced control functions, allowing our customers to maximize the value of their energy storage assets. Our service capabilities include advanced monitoring and analytics, scheduled maintenance, augmentation, and auxiliary system upgrades. The combination of excellence in battery technology and production, coupled with nearly two decades of energy storage integration, makes LGES Vertech a supplier and integrator in the power and energy markets.
LGES Vertech empowers and expects its team members to assume responsibility and make good decisions while maintaining a team environment that fosters collaboration and innovation. Our diverse and growing team enjoys competitive salaries, generous benefits, and flexible working hours.
For more information about LGESVT, please visit www.lgensol-vt.com.
Position Overview
LGES-Vertech is seeking a highly motivated Data Scientist to join our Data Science and Predictive Analytics team, where you'll be a key contributor in shaping intelligent, data-driven solutions for Battery Energy Storage Systems (BESS). You will play a key role in developing and deploying advanced machine learning solutions that address critical challenges in system performance, anomaly detection, diagnostics, and predictive maintenance for energy storage systems.
This role offers the opportunity to research and develop new algorithms, build scalable products, and directly influence how data science enhances the safety, availability, and commercial value of BESS systems.
Key Responsibilities Design and develop advanced machine learning solutions to detect anomalies, diagnose root causes, and forecast potential failures in Battery Energy Storage Systems (BESS). Take ownership of projects end-to-end from problem formulation and data exploration to model development, validation, and deployment in production environments. Build and maintain custom diagnostic and prognostic models that go beyond event detection to generate actionable insights for reliability, safety, and performance optimization. Collaborate closely with domain experts, data engineers, and DevOps to ensure models are integrated into scalable cloud-based pipelines. Lead research and prototyping of new methods, including unsupervised learning, statistical modeling, and signal processing techniques, to handle complex time-series and event data. Page 2 of 2 Analyze large-scale, imperfect, and noisy datasets from deployed field systems to uncover hidden patterns, trends, and failure modes. Contribute to and maintain a robust, modular codebase with clear documentation and versioning practices, following software engineering best practices. Support development of tools and visualizations to communicate diagnostics and predictions to engineering, operations, and customer teams. Job Requirements
Required Qualifications:
Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related quantitative discipline. 3-6 years of hands-on experience applying machine learning in signal processing, manufacturing, energy or other related industry. Demonstrated ability to independently develop and deploy custom machine learning algorithms, not just using off-the-shelf models. Proven expertise in anomaly detection, fault diagnostics, and system-level prognostics using time series and high-frequency data. Experience working with complex, noisy datasets from physical systems, with a strong emphasis on signal interpretation and pattern extraction. Strong Python development skills, including best practices in modular, scalable, and testable codebases. Deep understanding of both supervised and unsupervised learning methods; able to apply advanced models such as isolation forests, autoencoders, Bayesian inference, or graph-based methods. Familiarity with machine learning frameworks such as PyTorch, TensorFlow, or Scikit-learn. Comfortable working in a Linux environment and with Git-based workflows. Must be legally authorized to work in the U.S. without employer sponsorship.
Preferred: M.S or Ph.D. in Computer Science, Engineering, Physics, or a related field. Familiarity with cloud platforms: AWS, Azure, GCP, Databricks, or Snowflake. Familiarity with MLOps tools for model deployment, monitoring, and lifecycle management. Familiarity with CI/CD pipelines and integrating ML models into DevOps workflows. Familiarity with data engineering architecture and performing trade-off analyses using various cloud services. User-level experience with real-time monitoring and optimization of deployed pipelines. Experience in battery management systems, energy storage diagnostics, or predictive maintenance in the energy domain