Job purpose:
Develop and support implementation of strategies and plans for digital oilfields activities in line with company’s objectives and priorities, to enhance oil recovery, achieve operational excellence and promote safer operations. Develop and maintain the DOF strategy and benchmarks against ADNOC Group of Companies. Design, implement and maintain ADNOC’s offshore technical artificial intelligence strategy and data analytics framework in order to model complex ADNOC offshore problems and find opportunities by solving business related issues. Work with real-time data and perform big data mining while realizing the insights and identifying opportunities through the use of algorithms, statistical models and visualization techniques.
Key accountabilities:
Job specific accountabilities
Artificial intelligence
- Develop and maintain ADNOC’s offshore artificial intelligence strategy for the core business and technical domains related to exploration, development and production.
- Develop and maintain ADNOC’s offshore artificial intelligence guidelines document to provide general guidance and state the minimum requirements for technical departments and projects.
- Work closely with discipline engineers, subject matter experts and others in order to produce systems that utilize artificial intelligence and are capable of adapting or changing based on adding data.
- Conduct brainstorming sessions and technical workshops to identify opportunities to be assessed and prioritized, which can be solved or improved by implementing artificial intelligence tools.
- Conduct regular market surveys and be aware of different technological announcements (advancements) related to artificial intelligence and data science. Compare the top ranked technologies by Gartner’s and others and evaluate the technology functionality related to oil and gas industry.
- Develop scalable tools leveraging deep learning models to solve business problems in areas such as voice recognition, natural language processing and time series predictions.
- Suggest, collect and generate requirements. Create an effective roadmap towards the deployment of a production level artificial intelligence application.
Data science
- A data scientist capable to model complex problems while realizing ADNOC’s offshore insights and identifying opportunities through the use of algorithms, statistical models, data mining and visualization techniques.
- Recommend ongoing improvements to methods and algorithms that lead to findings, including new information. Provide business metrics for the overall project to reflect improvements by periodic monitoring over multiple iterations.
- Conduct workshops and sessions to help the technical departments understand the principles and math behind the process to drive organizational buy-in and to build multi-discipline teams that are capable to define, model and solve problems related to their domains.
- Design and launch innovative and complex analytic models and lead the development of big data capabilities and utilization as well as the coordination of cross-functional analytic initiatives.
- Work with different data types (sensors data, maintenance logs, datasheets, images, etc.) to build, validate and maintain predictive models.
- Educate the organization both from IT and the business perspectives on new approaches, such as testing hypotheses and statistical validation of results.
- Conduct scoping sessions, workshops and liaise with technical departments to identify matters to be analyzed using appropriate data and appropriate analysis tools.
- Provide ongoing tracking and monitoring of performance of decision support systems and provide algorithmic statistical models as a core.
- Conduct potential POCs, share the findings internally and identify opportunities to deploy pilot projects.
- Design and develop a scalable data analytics platform and solutions. Assess, benchmark and select data analytics technologies to be added to the big data platforms.
- Provide guidance and mentor developers regarding data analytics. Liaise with ADNOC to build and mentor a new team of data scientists.
Data preparation
- Identify what data is available and relevant, including internal and external data sources, leveraging and automating new data collection processes.
- Support in the application of patterns and variations in the volume, speed, variety and other characteristics of data supporting the data analytics initiative.
- Capture structured and unstructured data (e.g., images, voice, text, or metering data) required to build models related to oil and gas business.
General
- Provide input for preparation of the team/department budgets and assist in the implementation of the approved budget and work plans to deliver team objectives.
- Investigate and highlight any significant variances to support effective performance and cost control.
- Contribute to the achievement of the approved performance objectives for the team/department in line with the company performance framework.
- Design and implement new tools and techniques to improve the quality and efficiency of operational processes.
- Identify improvements in internal processes against best practices in pursuit of greater efficiency in line with ISO standards in order to define intelligent solutions for issues confronting the function.
Generic accountabilities
Supervision
- Plan, supervise and coordinate all activities in the assigned area to meet functional objectives.
- Train and develop the assigned staff on relevant skills to enable them to become proficient on the job and deliver the respective section objectives.
Budgets
- Provide input for preparation of the section/department budgets and assist in the implementation of the approved budget and work plans to deliver section objectives.
- Investigate and highlight any significant variances to support effective performance and cost control.
Policies, systems, processes & procedures
- Implement approved section/department policies, processes, systems, standards and procedures in order to support execution of the section’s/department work programs in line with company and international standards.
Performance management
- Contribute to the achievement of the approved performance objectives for the section/department in line with the company performance framework.
Innovation and continuous improvement
- Design and implement new tools and techniques to improve the quality and efficiency of operational processes.
- Identify improvements in internal processes against best practices in pursuit of greater efficiency in line with ISO standards in order to define intelligent solutions for issues confronting the function.
Health, safety, environment (HSE) and sustainability
- Comply with relevant HSE policies, procedures and controls and applicable legislation and sustainability guidelines in line with international standards, best practices and ADNOC code of practices.
Reports
- Provide inputs to prepare section MIS and progress reports for company management.
Communications & working relationships:
Internal
- Interactive working relationships with vice presidents, managers, team leaders, and various technical disciplines senior subject matter experts at stakeholders divisions in relation to identifying artificial intelligence (AI) solutions requirements acquisitions strategies.
- Frequent communications with procurement divisions for solutions acquisition and MSU contracts.
- Regular contacts with various other divisions’ personnel to discuss/exchange information and advise on relevant AI technologies/solutions including assets, drilling, production, corporate planning, etc.
- Daily interactions with other specialists.
- Regular contact with IT representatives.
- Occasional contact with counterparts in ADNOC and other group companies for exchange of information/knowledge, shared projects and project related experience.
- Regular contact with ADNOC HQ operations excellence, R&D and Panorama unit.
External
- Frequent contact with services providers, consultants, vendors etc.
Qualifications, experience, knowledge & skills:
Minimum qualification
- Bachelor’s degree in engineering, artificial intelligence, robotics, petroleum or equivalent discipline.
- Understanding of natural language processing (NLP), machine learning (ML) and artificial neural network (ANN).
- Strong experience with machine learning APIs and computational packages (TensorFlow, Theano, PyTorch, Keras, Scikit-Learn, NumPy, SciPy, Pandas, statsmodels).
- Knowledge of R, SQL and Python; familiarity with Scala, Java, C or C++, version control and Matlab is an asset.
Minimum experience & knowledge & skills
- Minimum 10 years of experience in large-scale data science, data analytics and software development within the oil & gas industry.
- Experience using and understanding business intelligence tools (e.g. Spotfire, Tableau).
- Experience with data mining and visualization tools.
- Experience with data preparation (pre-processing, feature engineering and feature selection), model training, application classification, regression.
- Experience supervised and unsupervised in machine learning algorithms.
- Experience with data capturing, aggregation, collation and manipulation with large data sets.
- Experience with big data and familiar with frameworks (e.g. Hadoop and Spark).
- Analytical mind, business insight and have strong math and statistics knowledge.
- Excellent communication and presentation skills.
- Proficient in English.
Professional certifications
- International certifications in project management, process improvements & standardisation, and/or technology implementation.
Technical competencies:
As per ADNOC standard competencies.
Behavioural competencies:
As per ADNOC standard competencies.