About the Role
The Data Scientist is responsible for designing, developing, and deploying advanced Machine Learning (ML) solutions for industrial applications such as reliability solutions, predictive maintenance, and operational/production optimization. The role focuses on building scalable, production-grade ML models that deliver measurable business value within complex Oil and Gas environments. This position requires strong domain expertise in Oil & Gas or Manufacturing industries, with a deep understanding of operational processes, asset performance, and industrial data ecosystems. The candidate will collaborate closely with engineers, subject matter experts, and business stakeholders to explore, validate, interpret, and operationalize data-driven solutions. The role demands both independent project leadership and effective cross-functional teamwork.
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Essential Functions
- Design, develop, and deploy Machine Learning algorithms for industrial use cases such as reliability monitoring and predictive maintenance.
- Develop supervised and unsupervised learning models including regression and classification techniques.
- Apply strong mathematical principles (linear algebra, calculus, probability, statistics) to model development and optimization.
- Develop scalable ML solutions using distributed computing frameworks (e.g., MapReduce, streaming technologies).
- Leverage domain expertise in Oil & Gas or Manufacturing to design context-aware predictive and prescriptive models.
- Collaborate with data engineers and subject matter experts to identify, validate, and interpret new data elements.
- Translate operational and industrial requirements into analytical and ML-driven solutions.
- Lead end-to-end data science initiatives from problem definition through deployment and monitoring.
- Develop rapid prototypes using Python, R, or JavaScript, with exposure to Java or Scala as a plus.
- Design and implement MLOps practices including CI/CD pipelines for ML models, automated testing, model versioning, containerisation, deployment automation, monitoring, and performance drift management.
- Ensure models are production-ready, robust, explainable, and aligned with operational constraints.
- Communicate technical insights clearly to both technical and non-technical stakeholders.
Education and Technical Qualifications
- MS in Computer Science, Electrical Engineering, Statistics, Engineering, or equivalent quantitative field.
- Proven applied Machine Learning experience (regression, classification, supervised and unsupervised learning).
- Strong mathematical foundation in linear algebra, calculus, probability, and statistics.
- Minimum 10 years of hands-on experience in the design, develop, deploy and operate Machine Learning algorithms for industrial use cases such as reliability monitoring and predictive maintenance.
- Mandatory experience in Oil & Gas or Manufacturing industry environments.
- Demonstrated domain expertise in industrial operations, asset management, process optimization, or predictive maintenance.
- Proven experience designing and deploying ML solutions in real-world industrial settings.
- Experience with scalable ML systems (e.g., MapReduce, streaming frameworks).
- Experience collaborating with cross-functional 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.
