Senior Data Scientist
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Key skills for this role
About the Role
Crescent Petroleum seeks a Senior Data Scientist to design, develop, and deploy advanced ML solutions for industrial applications in Oil & Gas. You will build production-grade models for predictive maintenance and operational optimization, requiring 10+ years of experience and strong domain expertise.
Key Skills for This Role
Responsibilities
- 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 to model development and optimization
- Develop scalable ML solutions using distributed computing frameworks
- 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
- Design and implement MLOps practices including CI/CD pipelines, 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
Requirements
- Minimum 10 years of hands on experience in designing, developing, deploying and operating 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
- 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
Full Job Posting
Job Description
- The Senior 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.
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.
Technical & Education Qualifications Requirement
- 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.
- 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.
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