Applicants must have 3 years of experience in the following: (1) Applying domain knowledge of oil and gas equipment and production systems to develop or deploy machine learning solutions using operational and sensor data for PHM, condition monitoring, or production optimization in production environments; (2) reliability analytics using operational, sensor, and event-based data; (3) deploying and operating machine learning models in production systems, including integration and execution for edge or near-edge applications; (4) integrating machine learning, deep learning, LLM-based systems, and visualization tools with production engineering workflows; (5) machine learning and deep learning model development for industrial assets, including predictive maintenance, anomaly detection, forecasting, and asset health monitoring in oil and gas production and engineering environments; (6) enterprise data science and cloud platforms, including Dataiku, Microsoft Azure, and Google Cloud Platform (GCP), to build and manage data pipelines, ML workflows, and GenAI applications; (7) Generative AI and RAG systems, including deploying and operating architectures combining LLMs with structured and unstructured data sources; and (8) building interactive dashboards and analytical interfaces using frameworks such as React, Angular, Dash, or Streamlit. Master's degree in Data Science, Computer Science, Computer and Information Science, Statistics, Engineering, Applied Mathematics, or a related STEM field, or foreign equivalent, plus 3 years of post-baccalaureate experience in the job offered or in data scientist, machine learning engineer, applied AI engineer, or related analytical job titles.