(Global Petroleum Refinery) Senior Data Artificial Intelligence Specialist
MatchaTalent
- Hobart, TAS
- Permanent
- Full-time
- Collaborate with cross-functional teams to drive data-driven improvements.
- Gather and preprocess data from various industrial sources.
- Analyze data to identify opportunities for process optimization, energy efficiency improvements, and cost reduction.
- Implement anomaly detection algorithms to identify abnormal patterns in industrial processes and equipment.
- Develop predictive maintenance models to minimize downtime.
- Apply image, video, and audio analytics to reduce manual efforts like visual inspection/field monitoring of industrial plants.
- Create informative dashboards to communicate insights to operational teams and management.
- Deploy machine learning models into production systems for real-time monitoring and decision-making.
- Maintain documentation for data analysis processes, models, and findings.
- Ensure data practices adhere to industry regulations and data privacy standards.
- Monitor and evaluate model performance, making necessary adjustments to maintain accuracy and reliability.
- This role required candidate to permanently relocate at Dhahran, Saudi Arabia.
- Hold a Bachelor's/Master's degree in Engineering or Data Science from a premier institute.
- Have at least 5 years of experience building data analytics-based products in industrial contexts, preferably in petroleum refineries.
- Ability to identify business problems addressable through data analysis and develop data-driven solutions, including engineering/operational issues and computer vision-based products.
- Possess exceptional analytical and problem-solving skills, with the ability to derive insights and opportunities from data.
- Understanding of the Oil & Gas downstream domain to apply data science effectively.
- Experience in developing AI/ML-based Digital twins of industrial processes/equipment.
- Experience in applying optimization algorithms in line with AI/ML models.
- Expertise in Python for data manipulation, analysis, and modeling, including popular libraries such as NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn, etc.
- Proficiency in deep learning tools such as Keras, TensorFlow, PyTorch, Caffe, etc.
- Proficiency in developing, evaluating, and selecting the best ML model based on the problem under consideration with an engineering sense.
- Ability to bring ideas from conceptualization to productionizing.
- Proficiency in visualization tools and packages, and in communicating data science topics to non-technical audiences.
- Basic project management skills to plan, execute, and deliver data science projects on time and within scope are preferred.