Data Scientist - Energy Trading

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Our core values — Stewardship, Character, Collaborate, Learn, Disrupt — are the lens through which we evaluate every business decision. As a dynamic, growing company that offers extremely competitive compensation and benefits, our employees are our most valued assets and the foundation of Expands performance among our E&P competitors.

We seek applicants from all backgrounds to ensure we get the best, most creative talent on our team. We realize that, historically, underrepresented groups feel the need to be 100% qualified in order to apply. If you meet any combination of our requirements, we encourage you to apply. We strive to hire people from a wide variety of backgrounds, not just because it’s the right thing to do, but because it makes our company stronger.

This isn't about maintaining existing systems - you'll be architecting the analytical foundation that spots major market shifts or dislocations and drives real commercial value. You'll lead the development of sophisticated models and analytical frameworks across natural gas, LNG, power, and related commodities while shaping the data strategy that supports critical trading decisions. The role combines hands‑on advanced modeling with strategic thinking about data acquisition and architecture, with the autonomy to steer the ship and move quickly as both you and the business scale up.

Reporting structure within Fundamental & Quantitative Analytics team will be determined based on team composition and individual experience levels.

• Lead end-to-end advanced modeling initiatives from identifying data needs and acquisition strategies (vendor sources, web scraping, internal systems, unstructured data) through to deployment of production models
• Develop and implement sophisticated analytical techniques including machine learning, time series forecasting, optimization models, and statistical analysis to address critical business themes with speed and accuracy
• Serve as the technical visionary for applying novel analytical techniques and emerging technologies including machine learning, artificial intelligence, and large language models to energy commodity challenges
• Collaborate closely with the Lead Data Engineer to develop integrated data strategy that supports both current analytical needs and cutting‑edge technological applications
• Collaborate closely with data engineers and analysts to translate complex business requirements into technical data solutions, ensuring seamless integration from data acquisition to model deployment
• Drive rapid response capabilities for market analysis, developing models and insights that can quickly adapt to changing market conditions and emerging opportunities
• Build and maintain advanced analytical outputs including predictive models, risk assessment tools, optimization algorithms, and real‑time monitoring systems
• Automate and streamline analytic processes and products
• Establish best practices for modeling processes (e.g. technical documentation, code review, version control, etc.)
• Partner with the Director and analytics team members, traders, and commercial stakeholders to identify high‑impact analytical opportunities and translate findings into actionable business intelligence
• Contribute to establishing best practices for model development, validation, and deployment while mentoring junior team members on advanced analytical techniques

• Strong background in machine learning, statistical modeling, optimization, and time series analysis with proven ability to apply these techniques to real‑world business problems
• Proficiency with advanced analytical tools including Python/R, SQL, machine learning frameworks (scikit‑learn, Tensor Flow, PyTorch), and cloud computing platforms
• Experience with diverse data acquisition and integration methods including APIs, web scraping; database management, schema development, and application of metadata layers; and working with both structured and unstructured data sources
• Strong understanding of data architecture principles and ability to contribute to strategic decisions about…