POSITION OVERVIEW

As an integral part of Hanwha Energy USA's Commodities team, the Data Scientist will oversee the entire data science pipeline for wholesale power and gas market applications including price forecasting, congestion analysis, load forecasting, and trading algorithms. This will encompass R&D, model development, pre- and post-model analytics, model deployment, and management of the ML model library. This role will leverage the latest AI/ML modeling techniques to directly impact the company's ability to make informed decisions, optimize operations, mitigate risks, and drive business growth.

The role requires a self-motivated, dedicated, and responsible individual with the ability to perform well under pressure collaboratively with a team of highly analytical and quantitative talent. Along with deep expertise in AI/ML modeling, the candidate should possess a solid understanding of power and gas market fundamentals - with particular emphasis on ERCOT and PJM markets - and emerging trends in wholesale energy.

The position will be based out of the Hanwha Energy USA Houston office, and the ideal candidate will be within commutable distance to the Houston office location.

RESPONSIBILITIES

• As part of Hanwha Energy USA's Commodities team, this position will play a pivotal role in developing innovative solutions to wholesale power and gas analytical needs - including price forecasting, congestion analysis, and trading algorithms - by applying state-of-the-art machine learning and predictive modeling techniques
• Responsible for coordinating the entire ML 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 efforts including integration into downstream business processes, tracking performance metrics, and maintaining model health in cloud environments
• Develop a comprehensive data visualization layer to enable intuitive understanding of analytical drivers by business stakeholders
• Working closely with the technology team and leveraging vast amounts of proprietary and market data, develop ways to extract business intelligence and actionable insights that can have a meaningful impact on the company's bottom line
• Communicate technical details of the modeling to key stakeholders and partners to drive impact and facilitate decision making

REQUIRED COMPETENCIES

• Analytical - Proven problem-solving capability with strong analytical skills including statistical analysis.
• Stakeholder Engagement - Understands the importance of seeking out relationships and working with others toward a shared goal.
• Effective Communication - Strong verbal and written communication skills.
• Agility - Demonstrate willingness to modify position as needed to meet the needs of the business.

REQUIRED QUALIFICATIONS

• Strong technical knowledge in deep learning and time series modeling - including GBM, LSTM, Transformer architectures, CNN, VAE, GAN and GNN - with demonstrated application in power or gas market contexts
• Minimum 3 years (Data Scientist) or 7 years (Senior Data Scientist) of industry experience applying modeling techniques in successful commercial energy applications
• Solid 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 (LLMs) - including practical application of LLMs for market intelligence, analytical summarization, retrieval-augmented generation (RAG), or workflow automation in a commercial setting
• An advanced degree, preferably Ph.D, in Engineering, Math, Physics, or a related field of study
• Strong problem-solving skills along with the ability to intuitively explain complex technical concepts to business stakeholders
• Strong knowledge in programming (Python, SQL), visualization tools (Plotly, PowerBI), ML packages (PyTorch, TensorFlow) and hyperparameter tuning (Optuna)
• Experience with cloud platforms (Azure or AWS) and MLOps practices - including model deployment, pipeline orchestration, experiment tracking, and model monitoring in production environments
• Proven track record in managing complex projects and leading cross-functional teams
• Excellent interpersonal skills with capability to work collaboratively with technical and non-technical teams
• Eligible to work in the USA for any employer without sponsorship

PREFERRED QUALIFICATIONS

• Experience with Graph Neural Networks (GNN) or graph-based modeling approaches - a significant differentiator for this role given the team's current AI initiative roadmap
• Direct experience in wholesale power or gas trading environments - understanding of how analytical outputs translate into commercial trading and hedging decisions
• Familiarity with ERCOT-specific datasets - 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 - pipeline flows, basis differentials, storage dynamics, and their interaction with power price formation
• Experience with MLOps tooling - MLflow, Airflow, Kubeflow, Azure ML, or AWS SageMaker - for production model lifecycle management

COMPENSATION: $140,000 - $180,000 salary

Attention external recruitment firms, we will not accept any unsolicited resumes at this time. Please do not contact any internal member of our company to discuss the position or to solicit candidates.