Production AI/ML Data Scientist — Cost Optimization

General Motors; United States; Hybrid; USD 120,000 - 190,000; Full time

Benefits offered by this job

Hybrid work model

Job summary

General Motors is seeking a Data Scientist to join the Product Program Cost Optimization AI/ML Solutions team. You will build production-grade data products and predictive models to improve cost intelligence and inform product decisions.

You will work across problem definition, data preparation, modeling, deployment, monitoring, and continuous improvement, collaborating with engineering, finance, and purchasing stakeholders to deliver measurable value.

Qualifications

• Bachelor’s degree or higher in a quantitative field.
• 5+ years in data science, ML, data engineering, or related roles.
• Advanced Python production-grade coding.
• Strong SQL and data pipeline experience.
• Experience deploying ML products in production.
• Experience with Databricks or similar platforms.

Responsibilities

• Partner with cross-functional teams to define problems, success metrics, and requirements.
• Develop predictive models for cost estimation and optimization.
• Build production-grade data pipelines integrating diverse data sources.
• Create reusable Python frameworks and data-processing patterns.
• Develop analytics dashboards, APIs, and visualizations for business decisions.
• Apply AI capabilities including LLMs and retrieval-augmented generation in cost-engineering contexts.
• Establish data quality controls, monitoring, and governance for production use.
• Communicate findings and business value to technical and non-technical stakeholders.

Skills

Python proficiency

SQL

Production ML

Data engineering

CI/CD

Communication

Education

Bachelor’s degree in CS/Data Science/Engineering

Tools

Databricks

Git

CI/CD tooling

Job description

General Motors is seeking a Data Scientist to join the Product Program Cost Optimization AI/ML Solutions team. You will build production-grade data products and predictive models to improve cost intelligence and inform product decisions.

You will work across problem definition, data preparation, modeling, deployment, monitoring, and continuous improvement, collaborating with engineering, finance, and purchasing stakeholders to deliver measurable value.