Predictive Analytics Engineer

About the role

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

Key responsibilities

• Develop and deploy production‑grade models for anomaly detection, failure prediction, remaining useful life estimation and process optimisation using high‑frequency sensor and operational data.
• Engineer scalable data and model pipelines with distributed and streaming technologies such as Spark, Kafka and MapReduce.
• Collaborate with reliability, process and maintenance engineers to translate operational challenges into analytical use cases and embed solutions into day‑to‑day decision making.
• Own end‑to‑end predictive analytics initiatives from problem framing and data exploration through deployment, adoption and performance monitoring.

Required profile

• Minimum 10 years of hands‑on experience designing, developing, deploying and operating predictive analytics and machine‑learning solutions in an industrial (oil & gas or manufacturing) environment.
• Strong domain expertise in asset integrity, asset performance management, process optimisation or reliability engineering.
• Advanced degree (MS) in Computer Science, Mechanical Engineering, Statistics, Engineering or a related quantitative field.

Required skills

• Proficiency in Python, R or JavaScript (experience with Java or Scala is a plus).
• Experience with Spark, Kafka, MapReduce and other distributed/streaming technologies.
• Solid foundation in linear algebra, calculus, probability and statistics applied to sensor and time‑series data.
• Knowledge of MLOps practices, CI/CD pipelines, model versioning, containerisation and production monitoring.