Energy Data Scientist - ML & Azure Production (Contract)

InterEx Group; Houston (TX); On-site; USD 83,000 - 138,000; Full time

Job summary

InterEx Group is seeking a Data Scientist for a contract, on-site role in Houston, Texas. You will apply data, statistics, and ML to solve real-world operational problems, collaborating with data engineers, AI teams and business stakeholders.

Responsibilities include building ML models, analyzing large datasets, turning insights into business solutions, and supporting AI/ML initiatives with production deployment focus.

Qualifications

• Strong hands-on Data Science / Machine Learning experience.
• Strong Python and SQL.
• Experience working with Azure data platforms.
• Microsoft Fabric experience is a big plus.
• Databricks experience is also beneficial.
• Oil & Gas / Energy experience is highly preferred.
• A scientific or technical background is also of interest; industry experience isn't essential if you have strong data science fundamentals.

Responsibilities

• Developing machine learning and statistical models
• Analysing large and complex datasets to identify trends, patterns and insights
• Building predictive models and turning them into practical business solutions
• Working with Azure and Microsoft Fabric data environments
• Supporting AI/ML initiatives and helping move models into production
• Working with data engineers to develop reliable data pipelines and datasets
• Communicating findings and recommendations to technical and business stakeholders

Skills

Data Science / Machine Learning

Python

SQL

Oil & Gas / Energy

Tools

Azure

Microsoft Fabric

Databricks

Job description

InterEx Group is seeking a Data Scientist for a contract, on-site role in Houston, Texas. You will apply data, statistics, and ML to solve real-world operational problems, collaborating with data engineers, AI teams and business stakeholders.

Responsibilities include building ML models, analyzing large datasets, turning insights into business solutions, and supporting AI/ML initiatives with production deployment focus.