How to apply for Senior Forecasting and Optimization Engineer

About EnergyHub

EnergyHub is a clean energy company that empowers utilities and consumers to manage distributed energy resources (DERs) like smart thermostats, EVs, and batteries. Their platform helps decarbonize the grid and is a leader in the growing virtual power plant space.

About the role

As a Senior Forecasting and Optimization Engineer, you'll build the AI/ML models that predict energy consumption and generation, and develop algorithms to control DERs optimally. Your work directly impacts grid reliability and renewable energy integration.

A typical day

You'll start by reviewing model performance dashboards, then dive into coding new features for forecasting algorithms. After a team standup, you might analyze a dataset from a utility partner and iterate on an optimization model. Afternoons often involve code reviews and collaborating with product managers to turn insights into product features.

Who EnergyHub is looking for

• Has 5+ years of experience in ML/stats/optimization with a focus on time series forecasting or control systems
• Deep Python expertise (Pandas, NumPy, SciPy, sklearn, PyTorch/TensorFlow) and has built production-grade data pipelines
• Strong mathematical foundation in optimization (linear programming, convex optimization) and probability/statistics
• Experience with large-scale relational databases (e.g., PostgreSQL) and cloud platforms (AWS/GCP/Azure)

Tips for this application

• Highlight specific projects where you developed energy forecasting or control algorithms, even if from a different domain (e.g., load forecasting, HVAC control)
• In your resume, quantify the impact of your models (e.g., improved forecast accuracy by X%, reduced energy costs by Y%)
• Mention experience with DERs or smart grid data (e.g., smart meter, solar, battery data) if you have any
• Showcase your ability to write production-level Python code by linking a GitHub repo with clean, well-documented code
• Tailor your cover letter to mention EnergyHub's mission and how your skills align with their focus on clean energy and grid optimization

What to cover in your cover letter

['Your passion for clean energy and how your work can directly impact climate change', "Specific examples of forecasting/optimization models you've built and their real-world outcomes", 'Your proficiency with Python and data pipelines, especially in production environments', 'Your ability to translate complex data into actionable insights for non-technical stakeholders']

Research before applying

• Read EnergyHub's blog posts and case studies on their DER management platform
• Understand the concept of virtual power plants and how EnergyHub's technology fits into the grid
• Look up recent news about EnergyHub (e.g., partnerships, funding, or grid integration projects)
• Familiarize yourself with common energy forecasting metrics (e.g., MAPE, RMSE) and optimization techniques (e.g., model predictive control)

Likely interview topics

Based on the job description, expect questions about:

• Walk me through a time series forecasting project: data preprocessing, model selection, evaluation metrics, and how you handled seasonality/trends
• How would you design an optimization algorithm to control a fleet of smart thermostats to reduce peak load?
• Explain a time you debugged a production ML model that started degrading in performance
• How do you handle missing or noisy data from distributed energy resources?
• Describe your experience with relational databases and building data pipelines for ML

Common mistakes to avoid

• Submitting a generic resume that doesn't highlight forecasting or optimization experience
• Failing to mention Python proficiency or not providing code samples when asked
• Overlooking the importance of production-grade code and data pipeline experience in your application