Data Scientist – Petroleum & Gas Description
We are seeking a Data Scientist to join the Reservoir Engineering Systems Division (RESD) of Petroleum Engineering Applications Services Department (PEASD). The primary role is to develop new IR4.0 solutions for Upstream users to address business challenges. The ideal candidate will have a strong focus on cutting-edge technology and a dedication to excellence. RESD is specialized in developing data-driven systems that optimize and streamline a large set of petroleum engineering processes in many PE areas such as reservoir management, reservoir description, and production engineering.
- Develop and implement machine learning models, predictive algorithms, and statistical analysis to solve specific challenges within the petroleum industry
- Conduct advanced data analysis, modeling, and visualization to extract meaningful insights from complex datasets
- Collaborate with cross-functional teams, including developers, project managers, and business stakeholders, to gather and understand system requirements
- Conduct system testing, debugging, and quality assurance to ensure robust and accurate data-driven solutions for petroleum processes
- Design and document system specifications, workflows, and user interfaces that incorporate data science techniques and methodologies tailored for petroleum applications
- Provide ongoing support and troubleshooting for data-related issues
- Collaborate in the full software development lifecycle, from concept and design to testing and deployment
- Stay updated on data science trends, emerging technologies, and best practices to continuously improve and innovate our systems and processes for petroleum applications
Data Scientist – Petroleum & Gas Qualifications
- Bachelors degree in Computer Science, IT, Data Science, or a related field
- 5 years of experience in Computer Science; at least 2 years in Data Science
- Knowledge and study in the fields of Data Mining and Machine Learning
- Strong knowledge and experience in working with big data, high dimensional data, sparse data, noisy data, and structured and unstructured data
- Knowledge of Data Science technologies and artificial intelligence techniques and tools