Energy Market Data Scientist — Forecasting & Trading AI

Hanwha Energy USA; Houston, Northern (TX, KY); Hybrid; USD 120,000 - 190,000; Full time

Job summary

Hanwha Energy USA, headquartered in Houston, seeks a Data Scientist to lead the full ML lifecycle for wholesale power and gas market applications. You will develop forecasting, congestion analysis, load forecasting, and trading algorithms, deploying models in production and monitoring performance in cloud environments.

Collaboration with the Commodities team and stakeholders is essential for impactful decision making. Ideal candidates have Ph.D.

Qualifications

• Ph.D. in a quantitative field is strongly preferred.
• 3+ years in data science for energy or 7+ years for senior role.
• Deep learning and time-series modeling experience in power or gas contexts.
• Strong Python/SQL, data visualization, and ML tooling proficiency.
• Experience with cloud and MLOps; able to explain complex concepts to stakeholders.

Responsibilities

• Lead ML lifecycle for wholesale power and gas analytics and forecasting.
• Develop, deploy, and maintain production forecasting and analytics models.
• Interpret drivers of price formation: dispatch, congestion, fuel, weather, load.
• Coordinate deployment into downstream processes and monitor model health.
• Build data visualization layers for business stakeholder insight.
• Collaborate with technology teams to extract actionable business intelligence.
• Communicate modeling details to stakeholders to drive decisions.
• Work within ERCOT/PJM contexts and emerging wholesale energy trends.

Skills

Analytical

Stakeholder engagement

Effective communication

Agility

Education

Ph.D. in Engineering/Math/Physics or related

Tools

Python

SQL

Plotly

PowerBI

PyTorch

TensorFlow

Optuna

Azure

AWS

MLflow

Airflow

Kubeflow

SageMaker

Job description

Hanwha Energy USA, headquartered in Houston, seeks a Data Scientist to lead the full ML lifecycle for wholesale power and gas market applications. You will develop forecasting, congestion analysis, load forecasting, and trading algorithms, deploying models in production and monitoring performance in cloud environments.

Collaboration with the Commodities team and stakeholders is essential for impactful decision making. Ideal candidates have Ph.D.