Data Scientist

Role overview

Machine learning and industrial analytics

• Design and deploy ML algorithms for reliability monitoring, predictive maintenance, and other industrial use cases.
• Develop supervised and unsupervised models, including regression and classification.
• Apply linear algebra, calculus, probability, and statistics to model development and optimization.
• Build scalable ML solutions using distributed computing frameworks such as MapReduce and streaming technologies.
• Design context-aware predictive and prescriptive models using Oil & Gas or Manufacturing domain expertise.
• Translate operational requirements into analytical and ML-driven solutions.

Delivery, collaboration, and MLOps

• Collaborate with data engineers and subject matter experts to identify, validate, and interpret data elements.
• Lead data science initiatives from problem definition through deployment and monitoring.
• Prototype using Python, R, or JavaScript; exposure to Java or Scala is a plus.
• Implement MLOps, including CI/CD, automated testing, model versioning, containerization, deployment automation, monitoring, and performance drift management.
• Ensure models are production-ready, robust, explainable, and aligned with operational constraints.
• Communicate technical insights to technical and non-technical stakeholders.

Education and experience

• Master’s degree in Computer Science, Electrical Engineering, Statistics, Engineering, or an equivalent quantitative field.
• Minimum 10 years of hands-on experience designing, developing, deploying, and operating ML algorithms for industrial use cases.
• Experience in Oil & Gas or Manufacturing industry environments is mandatory.
• Expertise in industrial operations, asset management, process optimization, or predictive maintenance.
• Proven applied ML experience in regression, classification, supervised learning, and unsupervised learning.
• Strong foundation in linear algebra, calculus, probability, and statistics.

Technical qualifications

• Proven experience designing and deploying ML solutions in real-world industrial settings.
• Experience with scalable ML systems such as MapReduce or streaming frameworks.
• Experience collaborating with industrial stakeholders, including engineers and subject matter experts.
• Experience using Python, R, or JavaScript; familiarity with Java or Scala is a plus.
• Experience working within modern development environments and AI-assisted workflows.