Company: Saudi Aramco
Skills: IT - Programming & Database, Petroleum Engineering
Education: Bachelors/3-5 yr Degree
Location: Saudi Arabia

Aramco energizes the world economy.

Aramco occupies a unique position in the global energy industry. We are the world's largest producer of hydrocarbons (oil and gas), with the lowest upstream carbon intensity of any major producer.

With our significant investment in technology and infrastructure, we strive to maximize the value of the energy we produce for the world along with a commitment to enhance Aramco's value to society.

Headquartered in the Kingdom of Saudi Arabia, and with offices around the world, we combine market discipline with a generations' spanning view of the future, born of our nine decades experience as responsible stewards of the Kingdom's vast hydrocarbon resources. This responsibility has driven us to deliver significant societal and economic benefits to not just the Kingdom, but also to a vast number of communities, economies, and countries that rely on the vital and reliable energy that we supply.

We are one of the most profitable companies in the world, as well as amongst the top five global companies by market capitalization.


Petroleum Engineering Application Services Department teams provide support and consultations to Upstream Business Line professionals for digital solutions related to IR4.0 technologies. These technologies have gained an increasing importance over the past few years and have become an essential factor in many Upstream workflows and business processes. Specifically, by capitalizing on the vast amount of data available in upstream and utilizing advanced Artificial Intelligence (AI) and Machine Learning (ML) algorithms, upstream users will be able to make informed decisions and perform thorough risk analysis, which will effectively address current and future Upstream business challenges and needs.

Key Responsibilities

You should be able to perform the following:

Work with customers to identify opportunities for leveraging data to drive upstream business solutions
Analyze data from various data sources to drive optimization and improvement of upstream business processes
Evaluate the effectiveness and accuracy of new data sources and data gathering techniques
Develop custom data models and algorithms to apply to data sets
Develop Machine Learning and AI models to optimize customer experiences and improve upstream business processes
Develop testing frameworks for testing model quality
Coordinate with different functional teams to integrate developed models with other systems
Develop processes and tools to monitor and analyze model performance

Minimum Requirements

As a successful candidate, you

Have a Bachelor degree in Computer Science, Statistics, Mathematics or related scientific/engineering field

Have strong knowledge in statistics, sensitivity analysis and stochastic modelling.

Have an experience in performing Exploratory Data Analysis and developing ML models using: Statistical languages/platforms (e.g. R, SPSS), Programming languages (e.g. python, Matlab, SQL, Business Intelligence and Analytics platforms (e.g. Spotfire, tableau etc.)

Have knowledge of a variety of machine learning techniques (supervised/unsupervised) and libraries (e.g. scikit-learn, KNIME and TensorFlow)

Working environment

Our high-performing employees are drawn by the challenging and rewarding professional, technical and industrial opportunities we offer, and are remunerated accordingly.

At Aramco, our people work on truly world-scale projects, supported by investment in capital and technology that is second to none. And because, as a global energy company, we are faced with addressing some of the world's biggest technical, logistical and environmental challenges, we invest heavily in talent development.

We have a proud history of educating and training our workforce over many decades. Employees at all levels are encouraged to improve their sector-specific knowledge and competencies through our workforce development programs - one of the largest in the world.