ExxonMobil Returnship Program

The ExxonMobil Returnship Program is a back-to-work program for experienced professionals who are on a career break for various reasons like childcare, trailing spouse, extended personal travel, etc. Our 12-week Company-paid, office-based program enables returners with a soft launch that includes networking sessions, mentoring, training and a project assignment. At the end of the program and based on a performance evaluation, shortlisted candidates may be offered full-time employment at ExxonMobil.

Last date to apply: On or before 16th July, 2024

https://jobs.exxonmobil.com/job-invite/78254/

Requirements:

- Minimum Bachelor's in Engineering from a recognized university with minimum 6.5 CGPA out of 10 or equivalent, for disciplines refer job descriptions below-

- Minimum of 3 years- full time work experience, currently must be on a career break while applying

- Self-motivated with demonstrated leadership and good communication skills

- Possess attributes such as attention to detail and good analytical skills

- Willing to work from office

Surface Group:

- Provides engineering expertise and support for ExxonMobil's assets worldwide, including greenfield and brownfield projects in Upstream, Downstream, and Low Carbon Solutions

- Bachelor's degree or higher in engineering for one of the following disciplines: Chemical, Petroleum, Mechanical, Pipeline, Civil, Materials, Metallurgy, Electrical/Electronics, Instrumentation & controls, Marine, or related disciplines from a recognized university

- Prefer candidates with experience in oil and gas, heavy manufacturing industry (Upstream, Refining, Petrochemical, Chemicals)

- Requires relevant experience in any of these: Process Design & Safety, Mechanical, Electrical, Instrumentation & Control, Civil & Structural, Operations & Maintenance, Reliability, Integrity, Flow Assurance & Subsea, Facilities, Materials, Pipeline & Riser

Sub-Surface Group:

- Supports geotechnical, geophysical and geological aspects across all business lines (Upstream, Product Solutions and Low Carbon Solutions), across all stages of asset life cycle (Concept Select to Operations)

- Bachelor's degree or higher in engineering for one of the following disciplines: Chemical, Petroleum, Mechanical, Geoscience, or related disciplines from a well-recognized university.

- Preference given to candidates with experience within oil and gas, heavy manufacturing industry (Upstream, Refining, Petrochemical, Chemicals).

What you will do in our team:

- Perform full life cycle management of resources ranging from exploring, developing and optimizing production to supporting end-of-field-life activities.

- Monitor reservoir data to analyze field performance and implement development plans. Execute opportunities for production debottlenecking and operations optimization.

- Contribute to all stages of seismic data conditioning & inversion

- Integrate geological and geophysical datasets, Construct petroleum reservoir models, optimize well placement, and evaluate alternative reservoir management scenarios, Design the well trajectories and data collection planning to optimize the well placement

Mods Group:

Modelling, Optimization, and Data Science Group:

- Utilizes computational modeling, mathematical optimization, and data science to develop and deploy cutting-edge technology that drives business decisions.

- Bachelors- or Masters- degree from a recognized university in one of the following disciplines: Chemical Engineering, Mechanical Engineering, or related disciplines. Candidates with experience in developing, applying, and analyzing physics- based models and/or data driven computational models and simulations will be preferred.

What you will do in our team:

- Work with computational scientists, data scientists, engineers, software developers, and geoscientists across the globe to develop, deliver and apply computational tools, models, or software to support our business.

- Enhance proprietary computational models and tools to address evolving business needs.

- Use machine learning, pattern recognition, deep learning, statistical analysis and data visualizations - along with domain knowledge and subject-specific models (E.g., physics-based). Design, build, and execute studies using proprietary or commercial tools to provide insights including calibrating models to field data and providing field optimization recommendations