To deliver cleaner energy solutions for a brighter tomorrow by guiding customers toward a better energy future, enabling customer choice, and providing market-leading energy solutions.
Requirements
- 1-3 years of experience using SQL to query large datasets in cloud-based data warehouses and data lakes
- 1-3 years of experience with machine learning and statistical methods, bonus points if experience is in time-series forecasting
- 1-3 years of experience in machine learning development in Python
- Fluency with data visualization to communicate complex topics in approachable ways
- Strong background in mathematics and statistics
- Bonus if you have experience with key data engineering frameworks: Apache Airflow, dbt, AWS services, Docker, Kubernetes
- Bonus if you have experience with software development lifecycle experience in GitHub (i.e. environment management, testing, deployment)
Responsibilities
- Dig deep into available data to identify patterns, create datasets for predictive models, and improve the accuracy and precision of existing forecasting services
- Apply state-of-the-art machine learning and predictive modeling techniques to provide a deeper understanding of our customers and improve accuracy of our load forecasts
- Integrate the outcomes of load forecasting models into downstream services, such as gross profit reporting, to address key business needs
- Develop key metrics and visualizations to monitor and assess the performance of load forecasts, ensuring their accuracy and reliability
- Communicate modeling and engineering technical detail to stakeholders and partners to drive impact and facilitate decision making
Other
- Excellent communication skills, with the ability to deliver complicated findings and explain technical approaches to a variety of audiences
- Required to be in-person in Houston, TX, through a hybrid workplace environment
- Flexible hybrid working schedule
- Training and coaching
- Tuition Reimbursement
