Senior Machine Learning Data Scientist
- You will be actively involved in the design, implementation, and continuous improvement of forecasting tools, models, and methodologies, while helping build a modern forecasting platform for the US power market. Your role will include validating forecast inputs and outputs, monitoring model performance from a data science standpoint, and ensuring model drift and forecast quality remain under control over time. You will also work cross-functionally within the broader forecasting community and with forecasting teams in other countries to promote knowledge sharing, consistency, and the cross-pollination of ideas and best practices
- You will be responsible not only for the technical development of forecasting solutions, but also for ensuring their operational reliability and relevance to business needs. This includes building models across the full lifecycle-from feature engineering, training, tuning, and execution to validation, monitoring, and ongoing refinement-while maintaining strong software engineering standards that deliver dependable, production-grade forecasts. Because these forecasts are used to support commercial decisions across ENGIE's US power business, reliability, traceability, and robustness are essential parts of the role.
- In addition to model development, you will provide scientific expertise for bespoke analyses and contribute to the continuous improvement of forecasting practices, performance measurement, and platform capabilities. A strong understanding of forecasting methodology, model governance, and production-quality software development is essential to succeed in this role, along with the ability to translate complex analytical outputs into practical business value for stakeholders across the organization.
- You hold a Bachelor's degree in a quantitative discipline such as Statistics, Mathematics, Computer Science, Engineering, Finance, or Economics, or a related field. In lieu of a degree, we will consider a combination of relevant experience that demonstrates strong quantitative rigor and practical business acumen
- You have the technical expertise to build, analyze, and productionize forecasting models, along with the communication skills needed to align diverse stakeholders around a clear and informed point of view
- A minimum of five (5) years of experience building and deploying forecasting models and data pipelines using Python and SQL, with ownership of production deliverables
- You hold a strong foundation in probability, statistics, data science, and machine learning, with practical experience in time series forecasting
- You have advanced proficiency in Python, SQL, Git, and modern data platforms such as Databricks and Spark, with the ability to build scalable data pipelines and automate workflows.
- You are knowledgeable in developing and deploying forecasting models, including regression, time series, and machine learning techniques, with experience improving forecast accuracy and backcasting performance
- You have experience designing and maintaining end-to-end forecasting systems including data ingestion, feature engineering, model training, hyperparameter tuning, deployment, and performance monitoring
- You are knowledgeable in energy markets, load forecasting, or commodity trading, and understand how market dynamics and regulatory changes impact forecasting outputs
- You are an effective communicator who can translate complex analytical insights into clear recommendations for both technical and non-technical stakeholders, including portfolio managers, traders, and risk teams
- You have strong analytical and problem-solving skills, with the ability to manage multiple priorities, work independently, and deliver accurate, high-quality results in a fast-paced environment
- This role is eligible for our hybrid work policy
- Must be willing and able to comply with all ENGIE ethics and safety policies
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