The Predictive Analytics Engineer is responsible for designing, building, and deploying predictive analytics solutions that improve asset reliability, enable predictive maintenance, and optimize operations and production across industrial facilities. The role centres on engineering robust, scalable, production-grade models and data pipelines, covering anomaly detection, failure prediction, remaining useful life estimation, and process optimization, that deliver measurable business value in complex Oil and Gas environments.

The position requires strong domain expertise in Oil & Gas or Manufacturing, with a solid understanding of operational processes, equipment behaviour, asset performance management, and industrial data sources such as process historians, sensor/IoT data, maintenance management systems, and inspection records. The engineer will work closely with reliability, process, and maintenance engineers, subject matter experts, and business stakeholders to translate operational problems into analytical use cases, then validate, interpret, and embed the resulting solutions into day-to-day operations and decision-making. The role calls for both independent ownership of projects from concept to deployment and effective collaboration across multidisciplinary teams.

Design, build, and deploy predictive analytics solutions for industrial use cases including reliability monitoring, condition-based and predictive maintenance, failure prediction, and remaining useful life estimation.

Develop and tune supervised and unsupervised models, such as regression, classification, clustering, anomaly detection, and time-series forecasting, using high-frequency sensor and operational data.

Apply strong mathematical and statistical foundations (linear algebra, calculus, probability, statistics, signal processing) to feature engineering, model development, and optimization.

Engineer scalable data and model pipelines using distributed and streaming technologies (e.g., Spark, Kafka, MapReduce) to process large volumes of process historian, IoT, and maintenance data

Use Oil & Gas or Manufacturing domain knowledge to build physics-aware, context-driven predictive and prescriptive models that reflect real equipment behaviour and operating conditions.

Work with data engineers, reliability and process engineers, and subject matter experts to identify, validate, cleanse, and interpret data sources such as historians, CMMS, inspection, and production records.

Translate operational, maintenance, and production challenges into well-defined analytical use cases with clear success criteria and business value.

Own predictive analytics initiatives end to end, from problem framing and data exploration through deployment, adoption, and ongoing performance monitoring.

The Predictive Analytics Engineer is responsible for designing, building, and deploying predictive analytics solutions that improve asset reliability, enable predictive maintenance, and optimize operations and production across industrial facilities. The role centres on engineering robust, scalable, production-grade models and data pipelines, covering anomaly detection, failure prediction, remaining useful life estimation, and process optimization, that deliver measurable business value in complex Oil and Gas environments.

The position requires strong domain expertise in Oil & Gas or Manufacturing, with a solid understanding of operational processes, equipment behaviour, asset performance management, and industrial data sources such as process historians, sensor/IoT data, maintenance management systems, and inspection records. The engineer will work closely with reliability, process, and maintenance engineers, subject matter experts, and business stakeholders to translate operational problems into analytical use cases, then validate, interpret, and embed the resulting solutions into day-to-day operations and decision-making. The role calls for both independent ownership of projects from concept to deployment and effective collaboration across multidisciplinary teams.

Design, build, and deploy predictive analytics solutions for industrial use cases including reliability monitoring, condition-based and predictive maintenance, failure prediction, and remaining useful life estimation.

Develop and tune supervised and unsupervised models, such as regression, classification, clustering, anomaly detection, and time-series forecasting, using high-frequency sensor and operational data.

Apply strong mathematical and statistical foundations (linear algebra, calculus, probability, statistics, signal processing) to feature engineering, model development, and optimization.

Engineer scalable data and model pipelines using distributed and streaming technologies (e.g., Spark, Kafka, MapReduce) to process large volumes of process historian, IoT, and maintenance data

Use Oil & Gas or Manufacturing domain knowledge to build physics-aware, context-driven predictive and prescriptive models that reflect real equipment behaviour and operating conditions.

Work with data engineers, reliability and process engineers, and subject matter experts to identify, validate, cleanse, and interpret data sources such as historians, CMMS, inspection, and production records.

Translate operational, maintenance, and production challenges into well-defined analytical use cases with clear success criteria and business value.

Own predictive analytics initiatives end to end, from problem framing and data exploration through deployment, adoption, and ongoing performance monitoring.

Predictive Analytics Engineer

Skills