Machine Learning Engineer – Digital Oilfield

Job Brief:

Are you experienced in developing and deploying ML solutions for subsurface and production engineering workflows with 10+ years of hands-on experience? Do you excel at transforming real-world, noisy operational data into high-accuracy time-series predictions and anomaly detection models? If so, this is the opportunity with one of our client!

Key Responsibilities:

• Design, build, train, and deploy production-grade ML models for applications including time series prediction, anomaly detection, and clustering within digital field systems.
• Collaborate closely with Senior Production Engineers and other domain experts to identify, scope, and prioritize high-value ML opportunities.
• Validate model accuracy and performance rigorously in operational settings and industrial environments.
• Partner with software engineering teams (ML Ops, Full Stack) to ensure seamless integration and scalability of deployed models.
• Conduct complex data modeling and feature engineering on diverse operational datasets.

Required Qualifications/Experience/Skills:

• 10+ years of experience in Machine Learning and data science.
• Strong background in developing models for time series analysis and anomaly detection.
• Expert proficiency in Python and core ML libraries (e.g., scikit-learn, TensorFlow/PyTorch).
• Deep understanding of data modeling, statistical analysis, and model validation techniques.
• Familiarity with subsurface or production engineering concepts is highly advantageous.

Job Location: Remote

Type of Employment: Permanent / Full time

Salary: Negotiable (based on experience)

What you can expect from the employer:

• Competitive salary based on experience.
• Work on cutting-edge digital oilfield initiatives.
• Collaborative and innovative environment.
• Remote flexibility and global exposure.

Job Types: Full-time, Permanent

Application Question(s):

• Do you have experience in building ML solutions for oil & gas applications?
• Have you worked with time-series and production data analytics?
• Are you proficient in deploying ML models to digital oilfield systems?
• Do you have 10 years of relevant experience in Machine Learning and energy analytics?