Data Scientist - Time Series Forecasting

What role you will play in the team

• You will be part of our exciting and rapidly expanding Kuala Lumpur Modeling, Optimization & Data Science team working and collaborating on a global scale providing deep technical insights and support to ExxonMobil’s Product Solution Business and supply chain/commercial activities
• As a key member of the team, you will collaborate with data scientists, data analysts, software developers, and business representatives from chemical, lubricants, and fuels value chains globally to develop, deliver, and apply data-driven tools, models, or software to support our businesses.
• You will be part of the team utilizing machine learning, time series analysis, pattern recognition, statistical analysis, design of experiments, and data visualizations, along with domain knowledge, to solve commercial problems and provide business insights.
• Position is based in Kuala Lumpur and not limited to Malaysian.

What you will do

• Demonstrate industry leadership at conferences or via publications/patents (when applicable)
• Develop level of expertise in specific technical discipline to be recognized as future leading global expert
• Supports discovery, development and deployment activities for the focused technology area consistent with the Technology Portfolio strategic vision and business partner goals
• Proactively engages with Principal Engineers to deploy (implement) technology within their specific technical discipline, consistent with site needs / opportunities
• Develop, deploy and act as the single point of contact for specific data science problems across multiple domains, technology support, uptake and value generation.
• Develop and evaluate new models using Timeseries forecasting techniques using python and/or integrating 3rd party tools with EM data/platforms
• Support development and execution of AI strategy consistent with the overall discipline vision and ensure alignment with business objectives
• Assist in developing and delivering technical training content
• Understand technology sustainment workflow and enhancements requests and develop new model/features and testing plan.

About you

Skills and Qualifications

• PhD degree in Data Science, Computer Science, IT, Applied Mathematics, Statistics, Engineering, or related disciplines with a minimum GPA of 3.5 (out of 4.0).
• At least 3 years of experiences of specialization in time series forecasting, in either research with publications, academics or industrial collaboration related to forecasting use cases
• Experience in developing, applying, and validating data-driven tools to model complex systems.
• Knowledge and practical experience in statistical analysis techniques (e.g., classification, regression, time-series, Bayesian techniques) and machine learning techniques (e.g., decision trees, ensemble methods, deep learning, neural networks, validation methods).
• Conceptual and mathematical understanding of algorithms, models, model assessment techniques, solution development and explainable AI.
• Proficiency in Python, including packages such as NumPy, pandas, scikit-learn, Keras, TensorFlow, and PyTorch.
• Familiarity with software testing and development practices (Agile).

Nice to have

• Supply chain, trading or commercial experience encompassing pricing, demand forecasting, and end-to-end value chain optimization are preferred.
• Prior experience in software development or working in software teams.
• Strong communication and interpersonal skills, with the ability to work collaboratively in a global team environment.
• Excellent problem-solving skills and attention to detail.

Your Benefits

We are committed to providing benefits that are competitive and support the needs of our employees.

Application Process

Please be informed that only shortlisted candidates will be notified, and you can find out more about our hiring process here.

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We thank you for your interest in ExxonMobil.

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Equal Opportunity Employer
ExxonMobil is an Equal Opportunity Employer. All qualified applicants will be duly considered for employment.

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