Gas, Power Data Science Sr Adv_TRGD1K

About the role

Data is at the heart of ENGIE’s mission to provide sustainable energy clients with superior insights that enable strategic, actionable trading and dispatch decisions. Every day, the Global Market Analysis team forecasts hundreds of millions of electric grid data points using a combination of physical modeling, statistics, and artificial intelligence. As a Senior Data Scientist, you will play a critical role in advancing ENGIE’s forecasting platform across multiple markets and time horizons. This position will focus on short-term power price forecasting and will work closely with battery trading and optimization teams to improve forecast accuracy, drive profitability, and support operational decision-making. The role requires strong analytical thinking, intellectual curiosity, and the ability to bridge physical system understanding with advanced computational modeling.

Responsibilities

• Analyze, develop, test, and improve forecasting models used to predict electricity prices and support trading decisions
• Own and enhance short-term forecasting workflows that generate updated forecasts throughout the day
• Partner closely with trading and optimization teams to evaluate forecast performance and determine the impact of forecast quality on trading profitability
• Expand forecasting capabilities across multiple U.S. power markets, including ERCOT, PJM, SPP, and other emerging opportunities
• Design and execute experiments to identify sources of forecast error and improve probabilistic forecasting performance
• Leverage statistics, machine learning, AI, and physical system models to build robust forecasting solutions
• Develop production-level Python code that supports large-scale model training, backtesting, and deployment in cloud-based environments
• Contribute to internal machine learning infrastructure, forecasting pipelines, and AI-enabled development tools
• Collaborate with a multidisciplinary team of experts in power systems, weather analytics, artificial intelligence, and market modeling

Requirements

• Bachelor's degree in Data Science, Computer Science, Engineering, Mathematics, Physics, Statistics, or another quantitative STEM discipline
• Minimum 5 years of experience working as a Data Scientist, Machine Learning Engineer, Quantitative Analyst, or similar role
• Minimum 10 years of relevant work experience
• Advanced proficiency in Python and experience developing production-quality code
• Strong knowledge of machine learning, statistical analysis, uncertainty quantification, and predictive modeling
• Strong understanding of linear algebra, probability, and experimentation methodologies
• Experience working with large datasets and building scalable analytical workflows
• Ability to communicate technical concepts, experimental results, and recommendations clearly to technical and business stakeholders
• Demonstrated critical thinking, problem-solving, and analytical capabilities
• Strong attention to detail and commitment to delivering high-quality solutions
• Team-oriented mindset with a collaborative approach to problem solving

Benefits

ENGIE offers a comprehensive benefits package including options for medical, dental, vision, life insurance, employer-paid short-term and long-term disability insurance, ESPP, generous paid time off including wellness days, holidays and leave programs, and a 401(k) Retirement Savings Plan with a company match. Supplemental benefits for full-time employees enhance emotional and physical well-being through all stages of life, from family forming to caregiver benefits.

Pay

Salary Range: $135,000 – $206,885 USD annually. This represents the average expected pay range for a qualified candidate. In addition to base pay, the position is eligible for a competitive bonus/incentive plan. Minimum Base Salary: $135,000.00 USD; Maximum Base Salary: $206,885.00 USD; Pay Basis: Annual.

Schedule

This role is eligible for the hybrid work policy; a minimum of 3 days working in the office per week is required. Must be available to travel domestically or internationally up to 10% of the time.