Sr. Data Scientist, Energy Systems

About this role

Description
POSITION DESCRIPTION:
We are seeking a highly skilled Senior Data Scientist with deep expertise in time series forecasting and MLOps to join our Grid Energy & Analytics team. This role sits at the intersection of statistical modeling, machine learning, and production of ML systems, with a strong emphasis on building reliable, scalable forecasting solutions that operate in real‑world, high‑stakes environments.


The ideal candidate is a hands‑on technical leader who can own forecasting problems from data preprocessing and feature engineering to model development, deployment, monitoring, and lifecycle management. You will play a key role in shaping our forecasting platform, improving model performance at scale, and establishing best‑in‑class MLOps practices that enable rapid experimentation while ensuring production reliability and governance on scale.


Responsibilities
Times Series Forecasting & Modeling

• Design, develop, and deploy high‑quality time series forecasts for energy market prices, ancillary services, energy demand, and renewable generation (e.g., solar PV and wind).
• Apply a broad range of forecasting methodologies, including classical statistical models, machine learning, and deep learning approaches, selecting methods appropriate to data regimes and business constraints.
• Lead feature engineering efforts incorporating calendar effects, weather signals, exogenous drivers, and regime changes.
• Establish rigorous model evaluation, backtesting, and benchmarking frameworks to ensure accuracy, robustness, and stability over time.

MLOps & Production ML Systems

• Architect, build, and maintain end‑to‑end MLOps pipelines, covering data validation, training, versioning, deployment, monitoring, and retraining.
• Ensure forecasting systems are scalable, observable, and reliable in production, with clear SLAs, alerting, and rollback strategies.
• Partner in the design and evolution of an internal forecasting platform t...