Data Scientist

Data Scientist – Price Optimization & Churn Forecasting Fully Remote Compensation: $65/Hour W2 Oil/Energy Overview This role focuses on price optimization, churn forecasting, and customer analytics to drive profitability and retention for NRG Consumer. The ideal candidate brings strong machine learning foundations, practical business modeling experience, and the ability to translate analytics into measurable business impact. The most recent hire in this space was a Data Scientist with a PhD in Economics and strong causal inference experience, which reflects the caliber and analytical rigor desired for this role. Project Overview Project Focus Price optimization Customer churn forecasting Profit and margin improvement Customer behavior and retention modeling Project Type Implementation and enhancement Production analytics and modeling Tech Stack Python PySpark Databricks Cloud-based data platforms Machine learning and statistical modeling libraries Job Summary Welcome to NRG and the epicenter of personal power. We’re driven by the idea of a smarter, cleaner, digitally-enhanced energy ecosystem—and the possibilities that brings to the world and to the seven million customers we serve. NRG Consumer data scientists blend technical expertise and business acumen to help NRG brands achieve financial and strategic objectives. This role is hands-on and learning-oriented, emphasizing real-world analytics and modeling. Key Responsibilities Adhere to NRG’s culture of humility, curiosity, and impact Process and analyze large datasets using Python, PySpark, and Databricks Develop predictive models and analytical dashboards Apply machine learning to real business problems such as churn, pricing, and forecasting Partner with business stakeholders to enable data-driven, profitable decisions Translate customer and prospect insights into actionable recommendations Must-Have Qualifications (Non-Negotiables) Machine Learning and Analytics Proven experience applying machine learning to real-world business problems Ability to clearly explain what business problems machine learning is solving, such as churn, pricing, forecasting, or personalization Retail or Consumer Analytics Experience Experience in one or more of the following areas: Customer churn and retention Pricing strategies and optimization Profit and margin improvement Subscription-based business models Customer loyalty and engagement Sales growth initiatives Business Impact Focus Demonstrated ability to use data science to improve customer experience and business profitability Technical Skills Strong Python proficiency with hands-on experience in PySpark, NumPy, pandas, scikit-learn, XGBoost, Matplotlib, and lifelines Communication and Collaboration Strong cross-functional communication skills Ability to work effectively with product, marketing, analytics, and business teams Comfortable communicating insights to both technical and non-technical audiences Preferred or Negotiable Qualifications PhD in a quantitative discipline such as Economics, Statistics, or Mathematics Deep knowledge of causal inference, forecasting, optimization, frequentist and Bayesian statistics, machine learning, and natural language processing Experience with version control Experience reading and writing cloud-based data Experience and Education Minimum Requirements 0 to 3 years of experience in data analysis or a related field Master’s degree in a quantitative discipline or equivalent practical experience Experience with statistical software and database languages