Description

You build and deploy predictive models for energy forecasting, anomaly detection, and operational optimization. You translate complex data into actionable insights for technical and non-technical stakeholders.

This role is on-site.

Responsibilities

• Develop predictive models for occupancy, runtime, cost forecasting, and anomaly detection to enable energy-efficient control.
• Optimize energy operations using data-driven insights for Demand Response, Time-of-Use shifting, and Virtual Power Plant strategies.
• Partner with data engineering to transform legacy data into robust, reusable data products for ML and real-time analytics.
• Embed intelligence into production systems by collaborating with product, engineering, and analytics teams.
• Communicate model outcomes and impact to diverse audiences, ensuring clarity for both technical and business stakeholders.

Required Skills

• 5+ years of experience in the energy industry with technical leadership on high-impact modeling initiatives.
• Strong proficiency in Python, including Pandas, NumPy, scikit-learn, and PySpark.
• Experience with distributed compute environments such as Spark, Databricks, and GCP.
• Proven expertise in predictive modeling, forecasting, and applied ML (regression, gradient boosting, time-series, causal inference).
• Understanding of energy markets, Distributed Energy Resources (DER), and Virtual Power Plant (VPP) concepts.
• Experience working with large-scale event and sensor data, preferably in 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