Data Scientist
Role Overview
Design and deploy scalable, production-grade machine-learning solutions for industrial applications, including reliability, predictive maintenance, and operational or production optimization.
Work with engineers, subject-matter experts, and business stakeholders to operationalize data-driven solutions in complex Oil & Gas or manufacturing environments.
Key Skills for This Role
Full Job Posting
Role overview
Design and deploy scalable, production-grade machine-learning solutions for industrial applications, including reliability, predictive maintenance, and operational or production optimization.
Work with engineers, subject-matter experts, and business stakeholders to operationalize data-driven solutions in complex Oil & Gas or manufacturing environments.
Core responsibilities
- Develop supervised and unsupervised models, including regression and classification, and apply mathematical principles to model development and optimization.
- Build scalable ML solutions using distributed computing frameworks such as MapReduce and streaming technologies.
- Lead end-to-end data-science initiatives from problem definition through deployment and monitoring.
- Develop prototypes using Python, R, or JavaScript; Java or Scala exposure is a plus.
- Implement MLOps practices covering CI/CD, testing, versioning, containerization, deployment automation, monitoring, and model drift.
- Ensure models are robust, explainable, production-ready, and aligned with operational constraints; communicate findings to varied stakeholders.
Education and experience
- MS in Computer Science, Electrical Engineering, Statistics, Engineering, or an equivalent quantitative field.
- At least 10 years of hands-on experience designing, developing, deploying, and operating ML algorithms for industrial use cases.
- Oil & Gas or manufacturing experience is mandatory, with expertise in industrial operations, asset management, process optimization, or predictive maintenance.
- Proven experience deploying ML solutions in industrial settings and experience with scalable ML systems.
- Experience collaborating with industrial engineers and subject-matter experts; experience in modern development environments and AI-assisted workflows.
Technical qualifications
- Applied machine-learning experience in regression, classification, supervised learning, and unsupervised learning.
- Strong foundation in linear algebra, calculus, probability, and statistics.
- Experience with Python, R, or JavaScript; familiarity with Java or Scala is a plus.
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