ExxonMobil

Produces oil, gas, fuels and chemical materials

Advanced Data Scientist

Applied Machine LearningConfirmed live in the last 24 hours

Full-timeBengaluru, Karnataka, IndiaNo salary listedIn-person

About the Job

Requirements

A Master's or Ph.D. degree from a recognized university in Data Science, Computer Science, IT, Chemical Engineering, Mechanical Engineering, Civil Engineering, Materials, Aerospace Engineering, Geoscience/Geophysics, Applied Mathematics, or a related discipline, with a minimum GPA of 7.0.
At least 5 years of relevant experience developing, delivering, and validating production-ready artificial intelligence and machine learning solutions.
Expertise in one or more of time series analysis, computer vision, natural language processing, generative artificial intelligence, and commercial analytics.
In-depth knowledge and practical experience in statistical analysis techniques, including classification, regression, time-series analysis, and Bayesian techniques, and machine learning techniques, including decision trees, ensemble methods, deep learning, neural networks, and causal analysis.
Practical experience with the full machine learning lifecycle, from problem formulation, data acquisition, and data cleaning through model building and enterprise-level deployment.
Proficiency in Python or R, machine learning frameworks such as PyTorch, TensorFlow, and scikit-learn, and libraries such as NumPy and pandas.
Experience with software engineering practices, agile methodologies, and version control using Git.
Ability to communicate and collaborate effectively in a global team environment.

Responsibilities

Work with data scientists, data analysts, computational engineers, machine learning engineers, software developers, and business representatives across the global organization to research, develop, and deliver data science tools, models, or software that solve business problems in the oil and gas industry.
Lead end-to-end delivery of artificial intelligence and machine learning solutions, including scoping, modeling, evaluation, deployment, and monitoring.
Develop generative artificial intelligence and natural language processing applications, and/or time-series, computer vision, and commercial analytics models.
Build production-ready solutions using machine learning operations best practices, including MLflow, continuous integration and continuous delivery, monitoring, and data quality practices.
Apply data science methods, machine learning tools, visualization, and statistical techniques with domain knowledge to generate actionable insights and optimized recommendations.

Desired Qualifications

Prior experience in oil and gas, commercial domains, supply chain, production systems, wells, or subsurface domains.
Experience working with Azure Databricks or other data science frameworks.
Experience with mathematical modeling, physics-based simulators, scientific computing, and numerical methods.

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 canwork together.

What Will You Do

Work with data scientists, data analysts, computational engineers, machine learning engineers, software developers, or business representatives across our global organization to research, develop, and deliver data science tools, models, or software for solving challenging business problems in the oil and gas industry.
Lead end-to-end delivery of AI/ML solutions: scoping, modeling, evaluation, deployment, and monitoring.
Develop GenAI/NLP applications, and/or time-series, computer vision, commercial analytics models.
Build production-ready solutions applying MLOps best practices (MLflow, CI/CD, monitoring, data quality).
Apply data science methods, machine learning tools, visualization and/or statistical techniques along with domain knowledge to generate actionable insights and provide optimized recommendations.

About You - Skills and Qualifications

Expertise in one or more of the following: Time Series Analysis, Computer Vision, Natural Language Processing, Generative AI, Commercial Analytics.
Master’s or Ph.D. degree from a recognized university in one of the following disciplines: Data Science, Computer Science, IT, Chemical Engineering, Mechanical, Civil, Materials, Aerospace, Geoscience/Geophysics, Applied Math or related disciplines with a minimum GPA of 7.0.
5+ years of relevant experience in developing, delivering, and validating production-ready AI/ML solutions.
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, causal analysis).
Practical experience in the full machine learning lifecycle from problem formulation, data acquisition, data cleaning to model building and deployment at enterprise level.Proficiency in Python or R, ML frameworks (PyTorch, TensorFlow, scikit-learn) and libraries (NumPy, pandas).
Experience with software engineering practices, agile methodologies and version control (Git).
Strong communication and interpersonal skills, with the ability to work collaboratively in a global team environment.

Key Skills

Applied Data Science
Statistical Modeling & Analysis
Machine Learning & Deep Learning
Generative AI, NLP, Computer Vision
Time Series Analysis & Forecasting
End‑to‑End ML Project Lifecycle
Python/R Programming Skills
Software Engineering & Agile Framework

Preferred Experience

Excellent problem-solving skill and attention to detail.
Prior experience with oil & gas, commercial domain, supply chain, production systems, wells or subsurface domain is highly desirable.
Experience working with Azure Databricks or other data science frameworks.
Experience with mathematical modeling, physics-based simulators, scientific computing and numerical methods would be an added advantage.

Functional Skills

Deep & Reinforcement Learning

Machine Learning

Bayesian & Causal Inference

Applied Software Engineering for Data

Mathematical Framing of Business Problems

Nothing herein is intended to override the corporate separateness of local entities. Working relationships discussed herein do not necessarily represent a reporting connection, but may reflect a functional guidance, stewardship, or service relationship.

Exxon Mobil Corporation has numerous affiliates, many with names that include ExxonMobil, Exxon, Esso and Mobil. For convenience and simplicity, those terms and terms like corporation, company, our, we and its are sometimes used as abbreviated references to specific affiliates or affiliate groups. Abbreviated references describing global or regional operational organizations and global or regional business lines are also sometimes used for convenience and simplicity. Similarly, ExxonMobil has business relationships with thousands of customers, suppliers, governments, and others. For convenience and simplicity, words like venture, joint venture, partnership, co-venturer, and partner are used to indicate business relationships involving common activities and interests, and those words may not indicate precise legal relationships.

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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