Digital Oilfields Specialist - AI Analytics

ADNOC Abu Dhabi UAE; Al Ruways Industrial City; On-site; AED 502,200 - 725,400; Full time

Benefits offered by this job

Exposure to advanced analytics and digital transformation projects

Job summary

ADNOC Abu Dhabi UAE is looking for a Digital Oilfields Specialist - AI Analytics in Abu Dhabi Emirate. The ideal candidate will drive digital transformation initiatives in the Oil and Energy sector, leveraging AI and big data technologies.

With a strong focus on data science, this position involves developing and supporting digital oilfield strategies. Candidates must have a minimum of 10 years of experience and a Bachelor's degree in a related field.

Qualifications

• Minimum 10 years of experience in large-scale data science, data analytics, AI, and software development within the Oil and Energy sector.
• Strong understanding of machine learning, statistical modeling, and predictive analytics.
• Experience with supervised and unsupervised learning.

Responsibilities

• Develop and support implementation of digital oilfield strategies.
• Design, implement, and maintain AI strategies for core business operations.
• Conduct market research on AI and emerging technologies in oil and gas.

Skills

Digital oilfields

Artificial intelligence

Data science

Machine learning

Big data analytics

Predictive modelling

Natural language processing

Data visualization

Education

Bachelor's Degree in Engineering, AI, Robotics, Computer Science, Data Science

Tools

TensorFlow

Python

SQL

R

Hadoop

Spark

Job description

Role Overview

Digital Oilfields Specialist - AI Analytics

Location: Abu Dhabi Emirate, United Arab Emirates

Industry: Oil and Energy

Function: Information Technology

Experience: Minimum 10 years in large‑scale data science, data analytics, AI, and software development within the Oil and Energy sector.

Job Type: Full‑time

Salary: 45,000–65,000

Key Responsibilities

• Develop and support implementation of digital oilfield strategies and plans in line with company objectives, operational priorities, and recovery improvement goals.
• Maintain the Digital Oilfields strategy and benchmark digital oilfield practices against ADNOC Group Companies.
• Design, implement, and maintain ADNOC Offshore artificial intelligence strategy for core business and technical domains related to exploration, development, drilling, and production.
• Develop AI strategy documents, technical guidelines, and minimum requirements for departments, programs, and digital oilfield projects.
• Work closely with discipline engineers, subject matter experts, operations teams, and IT stakeholders to develop AI systems that learn from data and adapt to operational conditions.
• Conduct brainstorming sessions and technical workshops to identify business and technical problems suitable for AI, machine learning, analytics, and automation.
• Prioritize AI opportunities based on value, feasibility, operational impact, data availability, and alignment with offshore business objectives.
• Conduct market research and technology scans covering artificial intelligence, machine learning, data science, analytics platforms, and emerging digital oilfield technologies.
• Compare and evaluate leading technologies, including industry‑ranked tools and solutions, for relevance to oil and gas operations.
• Develop scalable AI tools using deep learning models for use cases such as voice recognition, natural language processing, time series prediction, and operational decision support.
• Collect and define technical and business requirements for production‑level AI applications and prepare deployment roadmaps.
• Model complex offshore operational problems using algorithms, statistical models, data mining methods, and visualization techniques.
• Recommend continuous improvements to methods, algorithms, model structures, and analytical processes based on findings and new information.
• Define business metrics for AI and analytics projects and monitor improvements across multiple iterations.
• Conduct workshops to help technical departments understand data science principles, mathematical models, statistical validation, and AI‑driven problem solving.
• Build multidisciplinary teams capable of defining, modelling, and solving domain‑specific operational and technical challenges.
• Design and launch complex analytical models and lead the development of big data capabilities across cross‑functional initiatives.
• Work with structured and unstructured data, including sensor data, maintenance logs, datasheets, images, voice, text, and metering data.
• Build, validate, maintain, and monitor predictive models that support oil and gas business decisions.
• Educate IT and business stakeholders on hypothesis testing, statistical validation, model performance, data‑driven experimentation, and analytics adoption.
• Conduct scoping sessions and workshops with technical departments to select the right data, tools, methods, and analytics approaches.
• Track and monitor performance of decision support systems using algorithmic and statistical models.
• Conduct proof of concept initiatives, share findings internally, and identify opportunities for pilot project deployment.
• Design and develop scalable data analytics platforms and solutions for advanced offshore analytics use cases.
• Assess, benchmark, and select data analytics technologies for integration into big data platforms.
• Provide guidance and mentoring to developers and technical teams on data analytics design, model development, and implementation practices.
• Liaise with ADNOC teams to help build and mentor a new team of data scientists.
• Identify available and relevant data sources, including internal and external data, and automate data collection where practical.
• Support analysis of data volume, speed, variety, patterns, and other characteristics required for analytics initiatives.
• Capture, prepare, and process structured and unstructured data required to build models for oil and gas business challenges.
• Provide input for team and department budgets and support implementation of approved work plans.
• Investigate significant budget variances and highlight findings to support effective performance and cost control.
• Contribute to approved performance objectives for the team and department.
• Design and implement new tools and techniques to improve operational process quality, data‑driven efficiency, and analytical decision‑making.
• Identify process improvements against best practices and ISO standards to develop intelligent solutions for functional challenges.
• Plan, supervise, and coordinate assigned activities to meet functional objectives.
• Train and develop assigned staff to improve technical proficiency, analytics capability, and section performance.
• Implement approved policies, processes, systems, standards, and procedures in line with company and international standards.
• Comply with HSE policies, controls, sustainability guidelines, applicable legislation, and ADNOC Code of Practices.
• Provide inputs for section MIS reports, progress reports, management updates, and performance summaries.

Ideal Profile

• Bachelor's Degree in Engineering, Artificial Intelligence, Robotics, Petroleum, Computer Science, Data Science, or an equivalent discipline.
• Minimum 10 years of experience in large‑scale data science, data analytics, artificial intelligence, and software development within the Oil and Energy sector.
• Strong understanding of natural language processing, machine learning, artificial neural networks, deep learning, statistical modeling, and predictive analytics.
• Strong hands‑on experience with machine learning APIs and computational packages such as TensorFlow, Theano, PyTorch, Keras, Scikit‑Learn, NumPy, SciPy, Pandas, and statsmodels.
• Strong knowledge of Python, SQL, and R.
• Familiarity with Scala, Java, C, C++, version control, and Matlab is an advantage.
• Experience using business intelligence and visualization tools such as Spotfire, Tableau, or similar platforms.
• Experience with data mining, visualization, data preparation, preprocessing, feature engineering, feature selection, model training, classification, and regression.
• Experience with supervised and unsupervised machine learning algorithms.
• Strong background in data capture, aggregation, collation, manipulation, and analysis of large datasets.
• Experience with big data frameworks such as Hadoop and Spark.
• Practical ability to work with real‑time data, sensor data, maintenance logs, datasheets, images, voice, text, metering data, and other oil and gas data sources.
• Strong analytical mindset with solid mathematics, statistics, business insight, and problem‑solving capability.
• Experience building AI roadmaps, proof of concepts, pilot projects, decision support systems, and scalable production‑level analytics applications.
• Ability to work with discipline engineers, petroleum engineers, drilling teams, production teams, operations excellence groups, IT representatives, procurement teams, vendors, and consultants.
• Excellent communication, presentation, facilitation, and stakeholder engagement skills.
• Proficient in English with the ability to explain technical AI and analytics concepts to business and engineering audiences.
• International certifications in project management, process improvement, standardization, or technology implementation are advantageous.

Skills Set

• Digital oilfields
• Artificial intelligence
• Data science
• Data analytics
• Machine learning
• Deep learning
• Big data analytics
• Predictive modelling
• Statistical modelling
• Natural language processing
• Artificial neural networks
• Time series prediction
• Data mining
• Data visualization
• Decision support systems
• AI strategy
• AI guidelines
• Digital oilfield strategy
• Offshore analytics
• Production optimisation analytics
• Exploration analytics
• Development analytics
• Drilling analytics
• Sensor data analytics
• Real‑time data processing
• Structured data
• Unstructured data
• Feature engineering
• Feature selection
• Model training
• Classification
• Regression
• Supervised learning
• Unsupervised learning
• TensorFlow
• Theano
• PyTorch
• Keras
• Scikit‑Learn
• NumPy
• SciPy
• Pandas
• statsmodels
• Python
• SQL
• R
• Scala
• Java
• C
• C++
• Matlab
• Hadoop
• Spark
• Spotfire
• Tableau
• Proof of concept development
• Pilot project deployment
• Data platform design
• Technology benchmarking
• Vendor evaluation
• Technical workshops
• Stakeholder engagement
• MIS reporting
• HSE compliance
• ADNOC Code of Practices

Why Join Us

This opportunity is ideal for an experienced AI and data science professional who wants to drive digital oilfield transformation in Abu Dhabi Emirate, United Arab Emirates. The role offers exposure to advanced analytics, offshore production intelligence, AI strategy, big data platforms, predictive models, operational excellence, and cross‑functional digital transformation within the Oil and Energy sector. It is a strong career step for someone who can combine technical AI depth, oil and gas domain knowledge, real‑time data insight, and scalable solution delivery to improve safety, recovery, efficiency, and business performance.

About the Company

ADNOC Group is a leading energy company headquartered in Abu Dhabi, United Arab Emirates, with operations across the full oil and gas value chain. The group plays a major role in energy production, digital transformation, industrial development, and long‑term economic growth for the UAE. Through its offshore, operations excellence, research, digital, and technology functions, ADNOC Group offers professionals the opportunity to contribute to advanced Oil and Energy programs where artificial intelligence, data analytics, digital oilfields, operational safety, and production optimisation are essential to future‑ready performance.