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Entry-level Data Scientist, broad ML/LLM skills and metro hiring drive high applicant competition.
Core ML and data engineering skills transfer broadly, but drilling domain experience increases fit sensitivity.
Explicit 0–2 years plus mandatory Python/SQL/ML fundamentals create moderate shortlisting filters.
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Develop and maintain data pipelines that process one-second sensor data into actionable datasets for drilling analysis.
Build, test, and refine machine learning models focused on time-series challenges such as anomaly detection and failure prediction.
Create dashboards and tools to present model outputs effectively to field engineers and operational control center personnel.
Bachelor's degree in Data Science, Petroleum Engineering, Mechanical Engineering, or related quantitative field.
0–2 years of professional or internship experience in data science or analytical roles.
Proficiency in Python (including pandas/NumPy) and at least one ML framework like scikit-learn, XGBoost, or Langchain.
Working knowledge of SQL and ability to query large relational datasets.
Comfortable translating technical data insights into understandable formats for non-technical stakeholders.
Interested in or willing to learn drilling industry concepts and terminology on the job.
Experience or strong interest in working with time-series data, sensor/IoT data, and a mix of classical ML and newer LLM-based approaches.