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Hanwha Energy USA

Develops and operates utility-scale solar power

Data Scientist

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeHouston, TX, USA$140k - $180k

About the Job

Requirements

  • Demonstrated application of deep learning and time series modeling techniques, including gradient boosting machines, long short-term memory networks, Transformer architectures, convolutional neural networks, variational autoencoders, generative adversarial networks, and graph neural networks, in power or gas market contexts.
  • At least 3 years of industry experience applying modeling techniques in successful commercial energy applications for the Data Scientist level, or 7 years for the Senior Data Scientist level.
  • Understanding of ERCOT and PJM market fundamentals, including wholesale price formation, transmission congestion, nodal pricing, generation dispatch, and fuel market dynamics.
  • Understanding of transmission congestion in ERCOT and PJM, including how constraints bind, how congestion propagates across the network, and how shadow prices reflect the cost of binding constraints.
  • Experience with large language models, including practical commercial applications for market intelligence, analytical summarization, retrieval-augmented generation, or workflow automation.
  • An advanced degree in Engineering, Mathematics, Physics, or a related field of study.
  • Ability to explain complex technical concepts to business stakeholders.
  • Knowledge of Python, SQL, Plotly, Power BI, PyTorch, TensorFlow, and Optuna.
  • Experience with cloud platforms such as Azure or AWS and MLOps practices, including model deployment, pipeline orchestration, experiment tracking, and model monitoring in production environments.
  • Proven track record managing complex projects and leading cross-functional teams.
  • Eligible to work in the United States for any employer without sponsorship.

Responsibilities

  • Develop innovative solutions to wholesale power and gas analytical needs, including price forecasting, congestion analysis, and trading algorithms, using machine learning and predictive modeling techniques.
  • Coordinate the entire machine learning life cycle, including pre-model analytics, model selection, feature engineering, post-model evaluation, and model refinements across power and gas market applications.
  • Develop, maintain, and continuously improve production-grade forecasting and analytics models for wholesale power and gas markets, with a focus on ERCOT and PJM.
  • Build and interpret models that capture the key drivers of power and gas price formation, including generation dispatch, transmission congestion, fuel prices, weather, load patterns, and market participant behavior.
  • Coordinate model deployment, including integration into downstream business processes, performance metric tracking, and model health maintenance in cloud environments.
  • Develop a data visualization layer that enables business stakeholders to understand analytical drivers.
  • Work with the technology team and proprietary and market data to develop business intelligence and actionable insights that affect the company's bottom line.
  • Communicate modeling details to stakeholders and partners to support impact and decision-making.

Desired Qualifications

  • Ph.D. in Engineering, Mathematics, Physics, or a related field of study.
  • Experience with graph neural networks or graph-based modeling approaches.
  • Direct experience in wholesale power or gas trading environments, including understanding how analytical outputs translate into commercial trading and hedging decisions.
  • Familiarity with ERCOT-specific datasets, including nodal prices, shift factor matrices, constraint shadow prices, bid/offer disclosures, and generation outage feeds.
  • Experience with production cost modeling software such as DAYZER, PLEXOS, Aurora XMP, or PROMOD.
  • Familiarity with gas market fundamentals, including pipeline flows, basis differentials, storage dynamics, and their interaction with power price formation.
  • Experience with MLOps tools such as MLflow, Airflow, Kubeflow, Azure ML, or AWS SageMaker for production model lifecycle management.

About the Company

Hanwha Energy USA

Develops and operates utility-scale solar power

Hanwha Energy USA develops, builds, and operates utility-scale solar and energy storage projects to deliver clean, reliable power. By pairing solar generation with integrated storage, it smooths energy delivery and supports grid stability. It works with landowners, communities, investors, and corporate customers to manage projects from development through operation. Its differentiated approach is in-house management of the full lifecycle and expansion into data-center solar, green hydrogen, and other renewables to help customers decarbonize.

Company size
51-200
Company stage
Debt Financing
Total funding
$610M
Headquarters
Irvine, CA
Founded
2017

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