Data Scientist (Kuala Lumpur, Federal Territory of Kuala Lumpur, MY, 50088)

About us At ExxonMobil, our vision is to lead in energy innovations that advance modern living and a net-zero future. As one of the world’s largest publicly traded energy and chemical companies, we are powered by a unique and diverse workforce fueled by the pride in what we do and what we stand for. The success of our Upstream, Product Solutions and Low Carbon Solutions businesses is the result of the talent, curiosity and drive of our people. They bring solutions every day to optimize our strategy in energy, chemicals, lubricants and lower-emissions technologies. We invite you to bring your ideas to ExxonMobil to help create sustainable solutions that improve quality of life and meet society’s evolving needs. Learn more about our What and our Why and how we can work together . ExxonMobil in Malaysia In Malaysia, we host a global business and technology center in Kuala Lumpur that provides high-level information technology and engineering expertise to ExxonMobil’s Upstream, Product Solutions and Low Carbon Solutions businesses worldwide. In the upstream, we have interests in two Production Sharing Contracts with PETRONAS, while in the Product Solutions business, we supply a portfolio of specialty chemical products to the marketplace. What role you will play in the team You will be part of our exciting and rapidly expanding Kuala Lumpur MODS 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 and depending on your level of experience, you will be the single point of contact to improve business performances, or actively developing new models for integration and supporting our business lines in Product Solutions. Position is based in Kuala Lumpur and not limited to Malaysian What you will do Develop, deploy and act as the single point of contact for specific data science problems within Maintenance domain, technology support, uptake and value generation. Develop and evaluate new models using Timeseries modeling/NLP/Computer vision using python and/or integrating 3rd party tools with EM data/platforms Support Principal Engineer in development and execution of their technology area strategy consistent with the overall discipline vision and ensures alignment with business objectives Assist in developing and delivering technical training content Understand maintenance workflow and enhancements requests and develop new model/features and testing plan. 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 About you Skills and Qualifications Master’s, or 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 experience in developing, applying, and validating data-driven tools to model complex systems. In-depth 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). Practical experience in the full machine learning lifecycle, from problem formulation and data acquisition to model building and deployment at enterprise scale. Deep conceptual and mathematical understanding of algorithms, models, model assessment techniques, solution development and explainable AI. Extensive knowledge and experience in code design, testing, and ML Ops practices. Specialization in at least one sub-domain, such as time series forecasting, Bayesian skills or NLP-GenAI. Proficiency in Python, including packages such as NumPy, pandas, scikit-learn, Keras, TensorFlow, and PyTorch. Experience with software engineering practices, agile methodologies, DevOps, and version control. Experience working with Azure Databricks or other data science frameworks. Familiarity with software testing and development practices (Agile). Experience with data visualization tools (e.g., Tableau, Power BI). 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. Ability to identify and scope data science opportunities based on business needs. Strong communication and interpersonal skills, with the ability to work collaboratively in a global tea