Industrial Predictive Analytics Engineer for Asset Uptime

Crescent Petroleum; Sharjah; On-site; AED 420,000 - 660,000; Full time

Benefits offered by this job

Restaurant d'entreprise

Indemnités de stage/alternance

Job summary

Crescent Petroleum seeks a seasoned Predictive Analytics Engineer to design, build, and deploy predictive models that enhance asset reliability and optimize operations across industrial facilities.

You will develop and deploy models using Python/R/JavaScript, work with large-scale sensor data, and implement production-grade data pipelines and MLOps practices to ensure reliable, explainable results integrated with existing dashboards and systems.

Qualifications

• MS in Computer Science, Mechanical Engineering, Statistics, Engineering, or equivalent quantitative field.
• Proven hands-on experience developing and deploying predictive models in an industrial setting, including regression, classification, anomaly detection, and time-series forecasting using supervised and unsupervised learning techniques.
• Strong mathematical and statistical foundation in linear algebra, calculus, probability, and statistics, applied to sensor, process, and equipment performance data.
• Minimum 10 years of hands-on experience designing, developing, deploying, and operating predictive analytics and machine learning solutions for industrial use cases such as reliability monitoring, condition monitoring, and predictive maintenance.
• Mandatory experience in Oil & Gas or Manufacturing industry environments
• Demonstrated domain expertise in industrial operations, asset integrity and asset performance management, process optimization, or maintenance and reliability engineering.
• Proven track record of taking predictive models from prototype to sustained production use in real-world industrial settings, with measurable impact on uptime, maintenance cost, or production performance.
• Experience building scalable data and analytics pipelines using distributed and streaming technologies (e.g., Spark, Kafka, MapReduce) for high-volume sensor and time-series data.
• Experience working with industrial data sources such as process historians, SCADA/DCS, IoT platforms, and CMMS/EAM systems.
• Experience collaborating with cross-functional industrial stakeholders, including reliability, process, maintenance, and operations engineers and subject matter experts.
• Proficiency in Python, R, or JavaScript. Familiarity with Java or Scala is an advantage.
• Experience working in modern development environments, including version control, cloud platforms, and AI-assisted engineering workflows

Responsibilities

• 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 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.
• Develop rapid prototypes and proofs of concept in Python, R, or JavaScript. Experience with Java or Scala is an advantage.
• Design and implement MLOps practices, including CI/CD pipelines for models, automated testing, model versioning, containerisation, deployment automation, production monitoring, and model and data drift management.
• Ensure solutions are production-ready, reliable, explainable, and aligned with operational, safety, and integration constraints, including integration with existing dashboards, alerting, and asset performance management platforms.
• Communicate findings, model outputs, and recommendations clearly to technical and non-technical audiences, enabling operations and maintenance teams to act on predictive insights.

Skills

Predictive analytics

Python

R

JavaScript

Java/Scala

MLOps

Data pipelines

Spark

Kafka

Statistical modelling

Industrial domain knowledge

Education

MS in Computer Science or related quantitative field

Tools

MapReduce

Spark

Kafka

Job description

Crescent Petroleum seeks a seasoned Predictive Analytics Engineer to design, build, and deploy predictive models that enhance asset reliability and optimize operations across industrial facilities.

You will develop and deploy models using Python/R/JavaScript, work with large-scale sensor data, and implement production-grade data pipelines and MLOps practices to ensure reliable, explainable results integrated with existing dashboards and systems.