Job Specific Accountabilities
Artificial Intelligence:
Develop and maintain ADNOC’s offshore artificial intelligence strategy for exploration, development, and production core business and technical domains.
Create and update ADNOC’s offshore AI guidelines document to provide guidance and define minimum requirements for technical departments and projects.
Collaborate closely with discipline engineers, subject matter experts, and other stakeholders to produce adaptive AI systems capable of evolving with additional data.
Lead brainstorming sessions and technical workshops to identify opportunities for AI implementation, prioritizing projects for assessment and development.
Conduct regular market surveys on technological advancements related to AI and data science, benchmarking against top industry tools and evaluating their relevance to oil & gas operations.
Develop scalable AI tools leveraging deep learning models to address business challenges such as voice recognition, natural language processing, and time series prediction.
Gather, analyze, and translate requirements into a strategic roadmap for deploying production-level AI applications.
Data Science:
Model complex problems to derive insights and identify opportunities within ADNOC’s offshore operations using algorithms, statistical models, data mining, and visualization techniques.
Recommend improvements to methods and algorithms, monitoring KPIs to demonstrate ongoing value.
Conduct workshops and sessions to educate technical teams on data science principles, fostering organizational buy-in and building multidisciplinary teams capable of problem-solving within their domains.
Design and deploy advanced analytic models, leading initiatives for big data utilization and cross-functional analytics.
Work with diverse data types (sensor data, maintenance logs, datasheets, images, etc.) to build, validate, and maintain predictive models.
Educate both IT and business teams on emerging approaches such as hypothesis testing and statistical validation.
Lead scoping sessions and liaise with technical teams to identify suitable data and analysis tools for various projects.
Monitor performance of decision support systems, providing ongoing updates and statistical models as core components.
Conduct proof-of-concept (POC) projects, share findings, and identify opportunities for pilot deployments.
Design and develop scalable data analytics platforms; assess and benchmark new data technologies for integration into big data platforms.
Mentor and guide data science developers, collaborating with ADNOC to build and grow a skilled data science team.
Data Preparation:
Identify relevant internal and external data sources, automating data collection processes where possible.
Support handling of data characteristics such as volume, velocity, and variety to optimize analytics initiatives.
Capture structured and unstructured data (images, voice, text, metering data) necessary for modeling oil and gas operations.
Qualifications, Experience, Knowledge & Skills
Minimum Qualification:
Bachelor’s Degree in Engineering, Artificial Intelligence, Robotics, Petroleum, or an equivalent discipline.
Minimum Experience & Skills:
At least 10 years of extensive experience in large-scale Data Science, Data Analytics, and software development within the Oil & Gas industry.
Strong understanding of natural language processing (NLP), machine learning (ML), and artificial neural networks (ANN).
Proven experience with ML APIs and computational packages such as TensorFlow, Theano, PyTorch, Keras, Scikit-Learn, NumPy, SciPy, Pandas, and statsmodels.
Proficiency in R, SQL, and Python; familiarity with Scala, Java, C/C++, version control systems, and Matlab is advantageous.
Experience using business intelligence tools like Spotfire and Tableau.
Skilled in data mining, visualization, and handling large datasets.
Expertise in data pre-processing, feature engineering, model training, classification, and regression.
Hands-on experience with supervised and unsupervised machine learning algorithms.
Familiarity with Big Data frameworks such as Hadoop and Spark.
Strong analytical mindset, business acumen, and solid foundation in mathematics and statistics.
Excellent communication and presentation skills.
Proficiency in English.
Professional Certifications:
International certifications in Project Management, Process Improvement & Standardization, and/or Technology Implementation are preferred.
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