Senior Data Scientist

Overview

Contract - 9 Months (Potential Conversion or Extensions)

Lehi, UT (Onsite)

$45-55/hr

• 3-5 Years of experience
• Proven expertise in predictive modeling, forecasting, and applied machine learning techniques (e.g., regression, gradient boosting, time-series analysis, causal inference).
• Hands-on experience working with large-scale event and sensor data, ideally within energy, IoT, or device-driven ecosystems.
• Strong proficiency in Python (including Pandas, Num Py, scikit-learn, PySpark)
• Experience with distributed computing environments such as Spark, Databricks, or GCP.

• Expertise in energy forecasting, thermal/comfort modeling, or Demand Response optimization
• Deep understanding of energy markets, DER, and Virtual Power Plant (VPP) concepts
• Experience applying LLM or generative AI in analytics and optimization
• Advanced degree (MS/PhD) in a quantitative field
• 5+ years of industry experience with proven technical leadership on high-impact modeling initiatives

Responsibilities:

• Design and deploy advanced models for occupancy, runtime, cost forecasting, anomaly detection, and preconditioning to enable comfort-aware, energy-efficient control and maintenance.
• Leverage data-driven insights to enhance the accuracy and reliability of Demand Response (DR), Time-of-Use (TOU) shifting, and Virtual Power Plant (VPP) strategies.
• Collaborate with data engineering to modernize legacy structures into robust, documented, and reusable data products that support machine learning and real-time analytics.
• Partner with product, engineering, and analytics teams to embed intelligence into production systems and shape future data-driven energy experiences.
• Translate complex model outputs into clear, actionable recommendations for both technical and non-technical stakeholders.

One of Insight Global's clients is looking for a Staff Data Scientist to join their team. This individual will design and deploy predictive models that enable intelligent home energy decisions. They will build advanced models for occupancy, runtime, cost forecasting, and anomaly detection. Other responsibilities include optimizing energy operations by improving the precision of Demand Response, Time-of-Use shifting, and Virtual Power Plant strategies through data-driven insights.

They will work closely with data engineering teams to enhance data quality and scalability. Transforming legacy systems into robust, reusable products that support machine learning and real-time analytics. Additionally, they will translate complex model outputs into clear, actionable insights for both technical and non-technical Stakeholders. At times, they will explore advanced AI applications such as generative models and energy forecasting to push the boundaries of predictive analytics within the energy domain.