Natural Gas & Power Fundamental Analysis - Data Scientist

Overview

A large natural gas producer in Houston, which continues to build and expand its Commercial Trading/Origination teams for natural gas, LNG, and power, is seeking an experienced Data Scientist. The position is newly created for the Fundamental & Quantitative Analytics team; it will leverage energy data and advanced/novel modeling to drive insights that solve complex problems in underlying market fundamentals and drive growth in commercial operations revenue.

• Help to establish and architect an end-to-end advanced Data Science modeling function focused on data and models used for natural gas, LNG, and power trading and marketing/origination
• Working closely with the Data Engineer, develop and implement data acquisition strategies that incorporate vendor identification, web scraping, internal systems reviews, and unstructured data
• Lead model development using quantitative techniques, including machine learning, time series forecasting, optimization techniques, and statistical analysis
• Incorporate artificial intelligence and large language models
• Create robust processes and develop and deploy models into production with speed and accuracy
• Collaborate and coordinate with key stakeholders of Fundamental and Quantitative Analysis (Traders, Marketers/Originators, Commercial Analysts, IT, Data Engineers) to provide technical data solutions, including predictive models, risk assessment tools, optimization algorithms, and real-time monitoring systems
• Rapidly adapt models and insights to changing market conditions and emerging opportunities in natural gas, LNG, and power markets.

• Bachelor’s degree in a quantitative field (Mathematics, Engineering, Statistics, Computer Science, etc.)
• ~2+ years of experience in a data science or quantitative role with exposure to energy commodities (natural gas, power markets, crude oil, LNG, NGL, or related markets) is required
• Experience with natural gas market fundamentals, including basis trading, pipeline systems, storage dynamics, and location-specific pricing data sets, is highly desired. Candidates from adjacent energy markets with strong technical skills will be considered.
• Demonstrated experience with advanced modeling techniques in machine learning, statistical modeling, optimization, and time series analysis
• Advanced technical skills, including Python/R, SQL, machine learning frameworks (scikit-learn, Tensor Flow, PyTorch), and cloud computing platforms
• Experience with data acquisition methods, including APIs, web scraping, database management, schema development, and application of metadata layers
• Experience with both structured and unstructured data sources

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