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Generalist early-career Data Scientist role with ML/LLM breadth attracts moderate competition.
Industrial drilling context raises domain specificity, though core ML skills remain transferable.
Explicit 0–2 years plus mandatory Python, ML frameworks, and SQL increases candidate filtering.
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Build and maintain data pipelines transforming raw sensor data into analysis-ready datasets for drilling operations.
Develop, test, and iterate on machine learning models focused on time-series problems such as anomaly detection and failure prediction.
Create dashboards and tools to make model outputs actionable for field engineers and operators while supporting LLM-based workflows.
Bachelor's degree in Data Science, Petroleum/Mechanical Engineering, or related quantitative field.
0–2 years of professional experience with strong internship or analyst background in relevant areas.
Proficiency in Python (including pandas/NumPy) and at least one ML framework (e.g., scikit-learn, XGBoost, Langchain).
Working knowledge of SQL and ability to query large relational databases.
Comfortable translating complex engineering problems into technical tasks and communicating with SMEs and non-technical stakeholders.
Interest in or willingness to learn drilling domain concepts (e.g., ROP, MSE, BHA) on the job.
Experience or exposure to time-series data analysis and familiarity with cloud data platforms or LLM application patterns is a plus but not mandatory.