Lead Data Scientist — Commercial AI/ML Analytics

ExxonMobil; Spring (TX); On-site; USD 140,000 - 210,000; Full time

Benefits offered by this job

Pension Plan

Savings Plan with 7% match

Workplace Flexibility

Comprehensive medical, dental, and eye

Culture of Health

Employee Health Advisory Program

Disability Insurance

Job summary

ExxonMobil is seeking a seasoned AI/ML leader to design, develop, and deploy advanced analytics across pricing, marketing, supply chain and trading. You will work end-to-end from problem framing to production, partnering with business stakeholders to deliver scalable, reliable solutions.

The role emphasizes GenAI apps, time series, optimization, MLOps, governance and explainable AI, with mentoring and capability building across teams.

Qualifications

• 5+ years delivering production AI/ML solutions.
• Expertise in commercial analytics across pricing, marketing, supply chain or trading.
• Strong foundations in statistics and algorithm design.
• Proficient in Python and ML frameworks; experience with Databricks/Spark.

Responsibilities

• Lead end-to-end delivery of AI/ML solutions: scoping, modeling, evaluation, deployment, monitoring.
• Collaborate with cross-functional teams to translate business problems into mathematical frameworks.
• Build solutions in one or more domains with data quality, explainability, governance.
• Develop GenAI applications and/or time series, optimization, or analytics models.
• Ensure production readiness through MLOps practices (CI/CD, MLflow, monitoring, cost optimization).
• Mentor peers and contribute to internal AI capability building.

Skills

5+ years experience

Commercial analytics

Python & ML Frameworks

Databricks/Spark

GenAI frameworks

MLOps & Explainable AI

Communication skills

Education

Master’s or PhD in data science/CS/engineering

Tools

Databricks

Spark

Job description

ExxonMobil is seeking a seasoned AI/ML leader to design, develop, and deploy advanced analytics across pricing, marketing, supply chain and trading. You will work end-to-end from problem framing to production, partnering with business stakeholders to deliver scalable, reliable solutions.

The role emphasizes GenAI apps, time series, optimization, MLOps, governance and explainable AI, with mentoring and capability building across teams.