Machine Learning Engineer

Build statistical models and simulations that directly influence future Vehicle Order Guides (VOGs) at Stellantis. In this onsite role in Auburn Hills, MI, you will use a customer-level preference simulation approach to improve vehicle configuration optimization for upcoming model years.

What you will do

Design and run large-scale simulations, including scenarios such as 50,000 synthetic customers, to model vehicle purchase behavior. Your outputs will feed a customer-level preference simulation engine used to improve the optimization of Vehicle Order Guides (VOGs) for future model years.

• Develop statistical and machine learning models using Databricks
• Perform exploratory data analysis and feature engineering on complex datasets
• Translate model results into optimized VOGs that support product configuration decisions
• Collaborate closely with Data Engineering to refine and leverage curated datasets
• Communicate insights and model recommendations to business stakeholders
• Continuously evaluate and improve model accuracy and assumptions

You will work with datasets that may include:

• Historical vehicle sales
• Competitive sales data
• Feature-level willingness-to-pay data
• Customer preference models

What you bring

• Bachelor’s Degree required
• Minimum 5 years of experience in data science, machine learning, or applied statistics
• Strong experience with Databricks (critical requirement)
• Proficiency in Python including Pandas, NumPy, scikit-learn, and PySpark
• Strong SQL skills
• Solid foundation in statistical modeling, simulation techniques, and experimental design
• Experience translating analytical results into business decisions

Additional focus areas (preferred)

• Experience with choice modeling, conjoint analysis, or demand modeling
• Background in automotive, pricing, or product optimization analytics
• Experience working with large-scale simulation frameworks
• Familiarity with Spark and distributed computing
• Exposure to MLOps or model productionization

Technologies

Databricks, Python, Pandas, NumPy, scikit-learn, PySpark, SQL, Spark