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ExxonMobil

Produces oil, gas, fuels and chemical materials

Data Scientist

Applied Machine LearningUpdated on Nov 14
Full-timeKuala Lumpur, Federal Territory of Kuala Lumpur, MalaysiaNo salary listed

About the Job

Requirements

  • A Master's or PhD degree in Data Science, Computer Science, Information Technology, Applied Mathematics, Statistics, Engineering, or a related discipline, with a minimum GPA of 3.5 out of 4.0.
  • At least 3 years of experience developing, applying, and validating data-driven tools to model complex systems.
  • In-depth knowledge and practical experience in statistical analysis techniques, including classification, regression, time-series, and Bayesian techniques, and machine learning techniques, including decision trees, ensemble methods, deep learning, neural networks, and validation methods.
  • Practical experience across 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 machine learning operations practices.
  • Specialization in at least one sub-domain, such as time-series forecasting, Bayesian skills, or natural language processing and generative AI.
  • Proficiency in Python, including 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, including Agile.
  • Experience with data visualization tools such as Tableau or Power BI.

Responsibilities

  • Develop, deploy, and act as the single point of contact for specific data science problems within the maintenance domain, including technology support, uptake, and value generation.
  • Develop and evaluate new models using time-series modeling, natural language processing, or computer vision with Python, and/or integrate third-party tools with ExxonMobil data and platforms.
  • Support the Principal Engineer in developing and executing the technology-area strategy, consistent with the overall discipline vision and aligned with business objectives.
  • Assist in developing and delivering technical training content.
  • Understand maintenance workflows and enhancement requests, and develop new models or features and testing plans.
  • Demonstrate industry leadership at conferences or through publications or patents, when applicable.
  • Develop expertise in a specific technical discipline to be recognized as a future leading global expert.
  • Support discovery, development, and deployment activities for the focused technology area, consistent with the Technology Portfolio strategic vision and business partner goals.
  • Proactively engage with Principal Engineers to deploy technology within the relevant technical discipline, consistent with site needs and opportunities.

Desired Qualifications

  • Supply chain, trading, or commercial experience encompassing pricing, demand forecasting, and end-to-end value-chain optimization.
  • 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 team environment.
  • Problem-solving skills and attention to detail.

About the Company

ExxonMobil

Produces oil, gas, fuels and chemical materials

ExxonMobil produces oil and natural gas and manufactures fuels, lubricants, and chemicals for transport operators, industrial customers, and consumers around the world. Its businesses span resource exploration and production, refineries that turn crude oil into fuels, and plants that make plastics and other chemical materials. The company sells products through wholesale supply agreements and the Exxon, Esso, and Mobil brands, including motor oils for vehicles and machinery. It also sells services that capture, transport, and store industrial carbon dioxide underground. The merged company formed in 1999 from Exxon and Mobil, with roots in earlier oil businesses, and is headquartered in Spring, Texas.

Company size
10,001+
Company stage
IPO
Headquarters
Spring, TX
Founded
1999

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