Data Scientist

Data Scientist Brooksource Oil & Gas Client Houston, TX Overview

We’re seeking a Data Scientist to support the modernization and AI transformation of a leading drilling engineering software portfolio. This role sits at the intersection of data science, software engineering, and petroleum domain expertise—helping to bring legacy applications into the cloud while laying the foundation for advanced, AI-driven solutions.

This is a hands‑on, practical data science role — ideal for candidates with a background in computer science, engineering, or petroleum disciplines who can blend analytical skill with applied technical development.

• Develop, test, and deploy Python-based data science solutions that enhance and modernize existing drilling engineering applications.
• Support the implementation of AI and Agentic AI features — including document intelligence and natural language querying capabilities — with an emphasis on responsible AI guardrails.
• Collaborate with product managers, testers, data engineers, and domain experts to identify issues, validate models, and ensure outputs align with engineering requirements.
• Work with or supplement data engineers to prepare data for AI consumption, including indexing, cleaning, vectorizing, and other data readiness tasks.
• Contribute to the design of data pipelines and models that enable accurate, real‑time insights from drilling data.

• Bachelor’s or Master’s degree in a STEM discipline (Computer Science, Data Science, Petroleum Engineering, or related field).
• 2+ years of hands‑on experience in Python development for data science or engineering applications.
• Familiarity with cloud platforms (Azure preferred) and data engineering tools (e.g., Azure Foundry, API integrations).
• Strong problem‑solving skills and the ability to bridge data science concepts with real‑world engineering use cases.
• Background in drilling engineering or oilfield services environments.
• Demonstrated experience with responsible AI, retrieval‑augmented generation (RAG), or AI agent architectures.

• Experience commercializing AI projects in a B2B or B2C setting.
• Prior experience in modernization projects (migrating legacy systems to cloud or API frameworks).