Data Scientist

The ideal candidate's favorite words are learning, data, scale, and agility. You will leverage your strong collaboration skills and ability to extract valuable insights from highly complex data sets to ask the right questions and find the right answers. 

Key Responsibilities

• Design, develop, and deploy predictive models and advanced analytics solutions for oil & gas applications (production forecasting, reservoir optimization, predictive maintenance, and downstream operations).
• Perform data collection, cleaning, transformation, and exploratory analysis on structured and unstructured data.
• Build and maintain scalable data pipelines, dashboards, and visualizations to deliver actionable insights to stakeholders.
• Collaborate with petroleum engineers, geoscientists, operations teams, and IT to define problems, design solutions, and integrate analytics into business processes.
• Ensure high data quality and monitor model performance, retraining and optimizing as needed.
• Stay current with emerging tools, techniques, and industry trends in AI/ML, digital oilfields, and predictive analytics.

Qualifications & Requirements

• Bachelor’s degree in Petroleum Engineering, Computer Science, Data Science, Statistics, or a related field. Master’s or PhD preferred.
• 4–8 years of experience in data science or machine learning, with proven oil & gas domain expertise.
• Strong programming skills in Python, R, and SQL; experience with ML libraries and data visualization tools.
• Proven experience handling large, complex data sets (real-time and historical).
• Solid knowledge of oil & gas workflows (upstream, reservoir, drilling, production, or downstream).
• Strong communication skills with the ability to translate analytics into business impact.
• Experience deploying models into production environments.
• Must be willing to work on-site in Saudi Arabia and visit field sites as required.

Preferred Skills

• Advanced degree (MSc/PhD) in data science, applied mathematics, or engineering.
• Experience with optimization algorithms, IoT/SCADA systems, and real-time streaming data.
• Familiarity with cloud platforms (Azure, AWS, GCP) and big data frameworks (Spark, Hadoop).
• Exposure to geospatial, seismic, or reservoir modeling data.
• Experience mentoring or leading analytics teams.