Expert Data Scientist

ExxonMobil India Careers; Bengaluru; On-site; INR 3,500,000 - 6,000,000; Full time

Job summary

ExxonMobil India Careers is seeking a Senior AI Scientist to lead end-to-end enterprise AI/ML initiatives across global businesses. You will shape AI roadmaps, architect scalable, production‑ready solutions, and collaborate with data scientists, engineers, and business leaders to deliver measurable impact.

You will mentor teams, apply cutting‑edge AI research, and ensure governance, security, and responsible AI practices while advancing Degenerative AI, NLP, and computer vision capabilities at

Qualifications

• Master’s or Ph.D. in Data Science, CS, AI, Applied Math, Stats, Eng, Geoscience, or related with min GPA 7.0.
• 12+ years of industry experience deploying enterprise AI/ML solutions.
• Experience with Generative AI, LLMs, NLP, Time-Series Forecasting, CV, RL, and analytics.
• Strong programming in Python; APIs, Git, Agile.
• Experience on Azure, Databricks, Kubernetes, or similar.
• Leadership, communication, and stakeholder management.

Responsibilities

• Lead end-to-end delivery of enterprise AI/ML solutions from concept to production.
• Design and implement AI systems including Generative AI and NLP components.
• Build scalable AI platforms with MLOps, CI/CD, governance and monitoring.
• Translate complex problems into AI-driven solutions with measurable impact.
• Provide technical leadership on model architecture and experimentation.
• Partner with stakeholders to shape AI strategy and roadmaps.
• Mentor teams and promote responsible AI practices.
• Stay current with AI trends and evaluate new technologies.
• Drive adoption of modern AI engineering practices across the org.
• Ensure scalability, security, and governance of AI solutions.

Skills

Generative AI

LLMs

NLP

Time-Series Forecasting

Computer Vision

Reinforcement Learning

Commercial Analytics

Optimization

PyTorch

TensorFlow

scikit-learn

Hugging Face

LangChain

MLflow

Distributed Compute

Python

APIs

Git

Agile

Azure

Databricks

Kubernetes

Education

Master's or Ph.D. in Data Science / CS / AI

Tools

Azure

Databricks

Kubernetes

MLflow

LangChain

Hugging Face

Job description

• Reinforcement Learning

About Us

At ExxonMobil, our vision is to lead in energy innovations that advance modern living while reducing emissions. 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.

Job Group Capability

Data Science, Digital & Analytics

Job Group

Computational & Data Sciences

What Will You Do

As a Senior AI Scientist, you will provide technical leadership in the design, development, and deployment of transformational AI solutions that solve some of the most complex challenges across ExxonMobil’s global business. You will operate at the intersection of advanced AI research, enterprise‑scale implementation, and strategic business impact.

You will collaborate with data scientists, machine learning engineers, software developers, computational experts, and business leaders across the organization to shape AI roadmaps, architect scalable solutions, and deliver measurable value through advanced analytics and AI technologies.

Key responsibilities include:

• Lead the end‑to‑end delivery of enterprise AI/ML solutions, from opportunity identification and technical strategy through deployment, productionization, monitoring, and continuous improvement.
• Drive the design and implementation of advanced AI systems including Generative AI, agentic AI workflows, NLP, time‑series forecasting, computer vision, optimization, and commercial analytics solutions.
• Architect scalable and production‑ready AI platforms leveraging MLOps best practices including CI/CD, model governance, monitoring, observability, MLflow, and automated retraining pipelines.
• Apply advanced data science, machine learning, statistical analysis, and domain expertise to solve highly complex business and engineering challenges.
• Translate complex business and engineering problems into mathematical, statistical, and AI‑driven solutions with measurable operational or commercial impact.
• Provide deep technical leadership across model architecture, feature engineering, experimentation, evaluation methodologies, and AI system design.
• Partner with business stakeholders and senior leadership to define AI strategy, prioritize use cases, and align AI investments with enterprise objectives.
• Mentor and guide data scientists and AI practitioners by promoting best practices in applied AI, experimentation, software engineering, and responsible AI.
• Stay abreast of emerging AI technologies, research advancements, and industry trends, proactively evaluating and applying next‑generation AI capabilities to drive innovation and strategic business value.
• Evaluate emerging AI technologies, frameworks, and research trends to identify opportunities for innovation and competitive advantage.
• Drive adoption of modern AI engineering principles including scalable inference, distributed training, LLMOps, workflow orchestration, and cloud‑native AI architectures.
• Ensure solutions meet enterprise standards for scalability, security, reliability, governance, and responsible AI practices.

About You

Skills and Qualifications

• Master’s or Ph.D. degree from a recognized university in Data Science, Computer Science, Artificial Intelligence, Applied Mathematics, Statistics, Engineering, Geoscience/Geophysics, or related disciplines with a minimum GPA of 7.0.
• 12 + years of industry experience developing, deploying, and scaling enterprise‑grade AI/ML solutions in production environments.
• Demonstrated expertise in one or more of the following areas:
• Generative AI and Large Language Models (LLMs)
• Agentic AI systems and orchestration frameworks
• Natural Language Processing
• Time‑Series Forecasting
• Computer Vision
• Reinforcement Learning
• Commercial Analytics and Optimization
• Strong expertise in statistical learning, machine learning, deep learning, Bayesian methods, causal inference, and advanced optimization techniques.
• Proven experience leading end‑to‑end AI solution delivery from business problem formulation to deployment and operationalization at enterprise scale.
• Deep practical experience with modern AI/ML frameworks and ecosystems including PyTorch, TensorFlow, scikit‑learn, Hugging Face, LangChain, MLflow, and distributed compute environments.
• Strong programming expertise in Python and experience with modern software engineering practices, APIs, testing frameworks, Git, and Agile development methodologies.
• Experience deploying AI solutions on cloud and enterprise platforms such as Azure, Databricks, Kubernetes, or equivalent ecosystems.
• Strong communication, stakeholder management, and technical leadership skills with the ability to influence across technical and business organizations.
• Demonstrated passion for continuous learning and staying current with rapidly evolving AI technologies, tools, frameworks, and research trends.
• Demonstrated ability to lead ambiguity, drive innovation, and deliver high‑impact AI initiatives in complex enterprise environments.

Preferred Experience

• Experience applying AI solutions within oil & gas, energy, manufacturing, commercial optimization, supply chain, production systems, wells, or subsurface domains.
• Experience with scientific computing, optimization algorithms, numerical methods, physics‑based modeling, and digital twin technologies.
• Familiarity with modern GenAI ecosystems including vector databases, retrieval‑augmented generation (RAG), AI agents, and foundation model fine‑tuning.
• Experience leading cross‑functional AI initiatives across geographically distributed teams.
• Track record of mentoring technical teams and driving enterprise AI adoption.
• Publications, patents, open‑source contributions, or applied research experience in AI/ML‑related domains are considered an advantage.

Functional Skills

• Deep & Reinforcement Learning
• Machine Learning
• Bayesian & Causal Inference
• Applied Software Engineering for Data
• Mathematical Framing of Business Problems