Predictive Analytics Engineer

About the role

The Predictive Analytics Engineer will design, build, and deploy predictive analytics solutions that improve asset reliability, enable predictive maintenance, and optimise operations across industrial facilities in the oil and gas sector.

Key responsibilities

• Develop and deploy models for reliability monitoring, condition‑based maintenance, failure prediction and remaining useful life estimation.
• Engineer scalable data and model pipelines using distributed and streaming technologies such as Spark, Kafka and MapReduce.
• Collaborate with reliability, process and maintenance engineers to validate, cleanse and interpret data from historians, IoT sensors, CMMS and inspection records.
• Own end‑to‑end analytics initiatives from problem framing through deployment, adoption and performance monitoring.

Required profile

• MS in Computer Science, Mechanical Engineering, Statistics, Engineering or a related quantitative field.
• Minimum 10 years of hands‑on experience delivering predictive analytics and machine‑learning solutions in oil & gas or manufacturing environments.
• Strong mathematical and statistical foundation (linear algebra, calculus, probability, statistics, signal processing).
• Proven track record of moving models from prototype to sustained production with measurable impact on uptime or maintenance cost.

Required skills

• Python, R, JavaScript (Java or Scala a plus).
• Experience with Spark, Kafka, MapReduce, and other distributed/streaming platforms.
• MLOps practices: CI/CD pipelines, model versioning, containerisation, automated testing and drift management.
• Proficiency with version control, cloud platforms and modern development environments.