Agentic AI Data Scientist — AI-Powered Solutions

NRG Energy; Houston (TX); On-site; USD 90,000 - 140,000; Full time

Job summary

NRG Energy seeks a Data Science Analyst – Agentic AI Developer to design and deploy AI-powered solutions across the organization. You’ll combine data science, ML, software engineering, and agentic AI development to solve complex energy challenges.

You will work across data acquisition, model development, deployment, monitoring and continuous improvement, in a collaborative, fast-paced environment with stakeholders and engineers.

Qualifications

• Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Quantitative Finance, or related field; completion by Dec 2026; Master’s preferred.
• Zero or more years of experience in data science, ML, or analytics.

Responsibilities

• Collect, clean, integrate, and preprocess large datasets for analytics and AI applications.
• Design, develop, deploy, and optimize ML models for forecasting and anomaly detection.
• Build AI applications using LLMs, RAG, AI agents, APIs, and workflow automation.
• Prototype, test, and deploy production-ready AI solutions.
• Develop scalable data pipelines, APIs, and AI workflows.
• Collaborate with stakeholders to integrate AI into business workflows.
• Analyze data to identify trends and communicate findings via dashboards.
• Monitor and improve AI models for accuracy, scalability and governance.
• Stay current with AI/ML tech to drive innovation.

Skills

Python

SQL

ML frameworks

LLMs/RAG

AI agents

REST APIs

Git

Docker

Kubernetes

Education

Bachelor's degree in Data Science/CS/Stats/Engineering

Master’s degree preferred

Tools

LangGraph

LangChain

Spark

Databricks

MLflow

Kubernetes

Job description

NRG Energy seeks a Data Science Analyst – Agentic AI Developer to design and deploy AI-powered solutions across the organization. You’ll combine data science, ML, software engineering, and agentic AI development to solve complex energy challenges.

You will work across data acquisition, model development, deployment, monitoring and continuous improvement, in a collaborative, fast-paced environment with stakeholders and engineers.