Description

You will design and deploy predictive models that optimize home energy decisions, balancing comfort, cost, and demand-response strategies.

This role is on-site.

Responsibilities

• Build and deploy advanced models for occupancy, runtime, cost forecasting, anomaly detection, and preconditioning.
• Optimize energy operations by improving the reliability of Demand Response (DR), Time-of-Use (TOU) shifting, and Virtual Power Plant (VPP) strategies.
• Partner with data engineering to transform legacy data structures into documented, reusable data products for ML and real-time analytics.
• 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 experience in the energy industry with demonstrated technical leadership on high-impact modeling initiatives.
• Proven expertise in predictive modeling, forecasting, and applied ML, including regression, gradient boosting, time-series, and causal inference.
• Strong proficiency in Python (Pandas, NumPy, scikit-learn) and experience with 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.
• Experience working with large-scale event and sensor data, preferably within energy, IoT, or device-driven ecosystems.
• 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