Data Science Analyst - Agentic AI Developer

NRG Energy•Houston, TX

1d•$65,000 - $75,000•Onsite

About The Position

NRG Energy is seeking a Data Science Analyst – Agentic AI Developer to design, develop, and deploy AI-powered solutions that improve decision-making, automate business processes, and generate actionable insights across the organization. The role combines data science, machine learning, software engineering, and agentic AI development to address business challenges in the energy industry. The analyst contributes across the AI lifecycle, including data acquisition, model development, deployment, monitoring, and continuous improvement, in a collaborative, fast-paced environment. The position is fully in-office and based in Houston, Texas, or Princeton, New Jersey. Flexibility to work outside normal business hours may be needed to support critical business operations and project milestones. The base salary range is $65,000–$75,000. Other rewards may include annual bonus, short- and long-term incentives, and program-specific awards. The position may also be eligible for medical, vision, dental, 401(k), and flexible spending account benefits.

Requirements

Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Quantitative Finance, or a related quantitative discipline, completed or expected to be completed by December 2026.
Zero or more years of professional experience in data science, machine learning, AI application development, or advanced analytics.
Strong proficiency in Python and SQL, with experience processing large datasets and developing production-quality software.
Experience with machine learning frameworks such as Scikit-learn, TensorFlow, PyTorch, XGBoost, or similar technologies.
Experience developing AI applications using LLMs, prompt engineering, RAG, AI agent frameworks, vector databases, REST APIs, and modern software development tools such as Git and Docker.
Experience with cloud platforms (AWS, Azure, or Google Cloud), data visualization tools (Power BI, Tableau, Plotly, or Matplotlib), and building scalable data pipelines.
Strong understanding of statistics, feature engineering, model evaluation, and MLOps fundamentals.
Excellent analytical, communication, and problem-solving skills; ability to independently manage multiple priorities, collaborate across multidisciplinary teams, and deliver high-quality AI solutions with professionalism, integrity, and commitment to continuous learning.

Nice To Haves

Master’s degree.
Experience with LangGraph, LangChain, Semantic Kernel, CrewAI, or similar orchestration frameworks.
Experience with NLP, Generative AI, Computer Vision, Spark, Databricks, MLflow, Kubernetes, time-series forecasting, energy markets, or cloud-native AI services such as Azure OpenAI, Amazon Bedrock, or Google Vertex AI.

Responsibilities

Collect, clean, integrate, and preprocess large structured and unstructured datasets for advanced analytics, machine learning, and AI applications.
Design, develop, deploy, and optimize machine learning models for forecasting, predictive analytics, anomaly detection, recommendation systems, classification, and operational optimization.
Build AI-powered applications using large language models, Retrieval-Augmented Generation, AI agents, APIs, and workflow automation to improve productivity and streamline business processes.
Rapidly prototype, test, and deploy scalable AI solutions, translating ambiguous business challenges into practical, production-ready applications.
Develop scalable data pipelines, reusable software components, APIs, and AI workflows that integrate enterprise data sources and operational systems.
Collaborate with business stakeholders, software engineers, data engineers, analysts, and subject matter experts to integrate AI capabilities into business workflows.
Analyze complex datasets to identify trends, generate actionable insights, and communicate findings through dashboards, reports, and data visualizations for technical and non-technical audiences.
Monitor, evaluate, and continuously improve AI models and applications to ensure accuracy, scalability, reliability, governance, and responsible AI practices.
Stay current with emerging AI, machine learning, and data science technologies and evaluate opportunities to drive innovation and business value.