Data Scientist

Data Scientist

12 month contract

Onsite M-Th in Lehi, UT

W2 only

Summary

The company is redefining home energy intelligence through data and AI to enable personalized comfort, energy efficiency, and demand-response optimization across millions of connected homes. We are seeking a Staff Data Scientist to design and deploy predictive models that enable intelligent home energy decisions: from forecasting comfort and cost to optimizing EV charging and demand response events.

Duties

• Develop Predictive Models: Build and deploy advanced models for occupancy, runtime, cost forecasting, anomaly detection, and preconditioning to enable comfort-aware, energy-efficient control and maintenance.
• Optimize Energy Operations: Use data-driven insights to improve the reliability and precision of Demand Response (DR), Time-of-Use (TOU) shifting, and Virtual Power Plant (VPP) strategies.
• Advance Data Quality & Scalability: Partner with data engineering to transform legacy data structures into robust, documented, and reusable data products that support ML and real-time analytics.
• Cross-Functional Collaboration: Work closely with product, engineering, and analytics teams to embed intelligence into production systems and shape future data-driven energy experiences.
• Communicate Impact: Translate complex model outcomes into actionable insights for both technical and non-technical audiences.

Requirements

• 5+ years of energy industry experience, including demonstrated technical leadership on high-impact modeling initiatives.
• Experience with energy forecasting, thermal modeling, or Demand Response optimization.
• Understanding energy markets, Distributed Energy Resources (DER), and Virtual Power Plant (VPP) concepts.
• Proven expertise in predictive modeling, forecasting, and applied ML (e.g., regression, gradient boosting, time-series, causal inference).
• Experience working with large-scale event and sensor data, preferably within energy, IoT, or device-driven ecosystems.
• Strong proficiency in Python (Pandas, NumPy, scikit-learn, PySpark) and experience with distributed compute environments (Spark, Databricks, GCP).
• 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).
• Excellent communication and storytelling skills for complex, data-driven topics.

Preferred Qualifications

• 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.