Principal Data Scientist - AI & Optimization Leader

Equinor Company; Houston (TX); On-site; USD 180,000 - 240,000; Full time

Job summary

Equinor seeks a Principal Data Scientist to lead AI/ML initiatives across Houston, guiding complex problem solving from framing to deployment in operational settings. You will merge optimization, reinforcement learning, and agentic AI with safe, scalable delivery while aligning stakeholders around practical technical choices.

You will amplify business value by driving data pipelines, MLOps, and secure deployment, while mentoring teams and upholding safety and ethics in AI applications.

Qualifications

• Advanced degree in a quantitative field with research experience.
• Ability to translate ambiguous business questions into analytical problems and delivery plans.
• Experience delivering production-grade AI/ML solutions and managing data pipelines.

Responsibilities

• Drive complex AI deliveries from framing to implementation with stakeholder alignment.
• Lead technical tasks and coordinate multiple contributors in industrial settings.
• Design for bounded autonomy, confidence signaling, and auditable decisions.
• Improve performance, reliability and cost efficiency across model development and deployment.
• Coach teams on complex AI topics and adoption of safe, scalable AI.

Skills

AI/ML delivery

Data pipelines

MLOps

Cloud platforms

Secure deployment

Stakeholder management

Communication

Knowledge graphs

GenAI

Kubernetes

Education

Masters or PhD in OR/Math/CS/Data Science/Engineering

Operations research/Applied math/CS background

Tools

Azure

Databricks

Kubernetes

Python

CI/CD

MLflow

Job description

Equinor seeks a Principal Data Scientist to lead AI/ML initiatives across Houston, guiding complex problem solving from framing to deployment in operational settings. You will merge optimization, reinforcement learning, and agentic AI with safe, scalable delivery while aligning stakeholders around practical technical choices.

You will amplify business value by driving data pipelines, MLOps, and secure deployment, while mentoring teams and upholding safety and ethics in AI applications.