Jade Business Services (JBS)
Data Scientist
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Staff Data Scientist with a contract length of "unknown" and a pay rate of "unknown," located in "unknown." Key skills required include predictive modeling, Python proficiency, and experience in energy or IoT sectors. An advanced degree and 5+ years of industry experience are preferred.
🌎 - Country
United States
💱 - Currency
$ USD
-
💰 - Day rate
Unknown
-
🗓️ - Date
November 21, 2025
🕒 - Duration
Unknown
-
🏝️ - Location
Unknown
-
📄 - Contract
Unknown
-
🔒 - Security
Unknown
-
📍 - Location detailed
Salt Lake City, UT
-
🧠 - Skills detailed
#Data Engineering #Python #Storytelling #GCP (Google Cloud Platform) #Computer Science #"ETL (Extract #Transform #Load)" #Anomaly Detection #Statistics #Pandas #Regression #Leadership #Data Quality #Databricks #Forecasting #A/B Testing #Spark (Apache Spark) #IoT (Internet of Things) #AI (Artificial Intelligence) #PySpark #ML (Machine Learning) #NumPy #Scala #Data Science #Predictive Modeling
Role description
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.
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.
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.
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.
