Job Description
Our client operates at the crossroads of energy and home services, fueled by the vision of a smarter, cleaner future. Committed to developing groundbreaking solutions, they aim to streamline their customers’ lives by providing energy, protection, and smart services for their homes and businesses.Our client is 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.
- 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.
Required Qualifications
- 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
- Experience with energy forecasting, thermal modeling, or Demand Response optimization.
- Understanding energy markets, Distributed Energy Resources (DER), and Virtual Power Plant (VPP) concepts.
- 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.
- 5+ years of industry experience, including demonstrated technical leadership on high-impact modeling initiatives.