Description

Design and deploy predictive models for home energy optimization, covering forecasting, anomaly detection, and demand response strategies.

This role is on-site.

Responsibilities

• Build and deploy advanced predictive models for occupancy, runtime, cost forecasting, and preconditioning.
• Optimize Demand Response (DR), Time-of-Use (TOU) shifting, and Virtual Power Plant (VPP) strategies using data-driven insights.
• Transform legacy data structures into robust, documented, and reusable data products in partnership with data engineering.
• Embed intelligence into production systems by collaborating with product, engineering, and analytics teams.
• Translate complex model outcomes into actionable insights for technical and non-technical audiences.

Required Skills

• 5+ years of energy industry experience with demonstrated technical leadership on high-impact modeling initiatives.
• Proven expertise in predictive modeling, forecasting, and applied ML (regression, gradient boosting, time-series, causal inference).
• Strong proficiency in Python (Pandas, NumPy, scikit-learn) and distributed compute environments (Spark, Databricks, GCP).
• Experience with energy forecasting, thermal modeling, or Demand Response optimization.
• Understanding of energy markets, Distributed Energy Resources (DER), and Virtual Power Plant (VPP) concepts.
• Ability to take models from concept to production in collaboration with engineering partners.
• Skilled in statistical analysis, feature engineering, and experimental design (e.g., A/B testing).

Preferred Skills

• Familiarity with LLM or generative AI applications in analytics and optimization.
• Advanced degree (MS/PhD) in a quantitative field such as Statistics, Computer Science, or Engineering.

Key Skills

Education

Any Graduate