We're seeking a sharp, highly analytical individual to take ownership of advanced forecasting and optimization efforts in a unique, energy-focused environment. This role is heavily centered on time series modeling and algorithm design. You'll be building models that directly inform the real-time operation of large-scale systems—not constructing pipelines, managing infrastructure, or deploying models in production environments.
Your focus will be on creating accurate, adaptable forecasting tools and optimization logic. We’re specifically looking for someone with deep expertise in time series forecasting and a passion for applying modeling techniques to complex, data-rich problems. This is not a fit for candidates whose strengths are in data engineering, MLOps, or infrastructure.
In This Role, You'll:
- Collaborate closely with cross-functional teams to ensure your models align with operational and business objectives.
- Create optimization routines to drive intelligent control strategies for energy storage and load scheduling.
- Incorporate new and external data sources to strengthen forecasting reliability.
- Keep up with recent advancements in forecasting, ML, and algorithmic optimization to inform your work.
- Work with diverse datasets to continuously improve model accuracy and responsiveness.
- Write clean, maintainable, and efficient Python code focused on modeling and analytics.
- Develop and refine time series forecasting models for key metrics such as energy pricing, datacenter load, and renewable generation.
Who We're Looking For:
- Strong communication and collaboration skills.
- Masters or PhD in Mathematics, Machine Learning, Statistics, or a related quantitative field.
- Comfortable working independently on modeling tasks, without the need to build or maintain data pipelines or infrastructure.
- Background in applied math, machine learning, statistics, or related fields.
- Strong understanding of model validation, backtesting, and performance metrics specific to forecasting.
- Excellent problem-solving skills and the ability to translate messy real-world data into clear modeling approaches.
- Proficiency in Python and ML libraries like PyTorch or TensorFlow.
- Interest in energy systems, sustainability, or large-scale resource optimization is a plus.
- Extensive experience with time series modeling techniques (ARIMA, Prophet, RNNs, LSTMs, Transformers, etc.).
