Senior Data Scientist

Crescent Petroleum; Sharjah; On-site; AED 350,000 - 500,000; Full time

Job summary

Crescent Petroleum seeks a Senior Data Scientist to design, develop, and deploy advanced ML solutions for industrial assets in Oil & Gas. You will build production-grade models for reliability monitoring, predictive maintenance, and optimization of operations.

The role requires deep domain expertise and leadership to deliver robust, explainable ML solutions. You will collaborate with engineers and stakeholders, establish MLOps practices, and drive end-to-end deployment and monitoring of models.

Qualifications

• 10+ years hands-on ML design, development, deployment for industrial use cases.
• Mandatory Oil & Gas or Manufacturing experience.
• Domain expertise in asset management, process optimization, or predictive maintenance.
• Proven ability to deploy ML solutions in real-world industrial settings.

Responsibilities

• Design, develop, and deploy ML algorithms for reliability monitoring and predictive maintenance.
• Develop supervised and unsupervised models including regression and classification.
• Apply advanced math (linear algebra, calculus, statistics) to model development.
• Lead end-to-end data science initiatives from problem to deployment and monitoring.

Skills

Machine Learning

Python

R

Data analysis

MLOps

Education

MS in Computer Science / Electrical Engineering / Statistics

Tools

MapReduce

Streaming frameworks

Python

R

JavaScript

Job description

Major Functions

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.

Major Functions

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. 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.

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