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# Machine Learning Engineer

- **Company:** Fiat Chrysler Automobiles N.V.
- **Location:** Auburn Hills, MI, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Information Engineering, Database Queries, Distributed Systems, Python (Programming Language), Machine Learning, NumPy, Feature Engineering, Apache Spark, Pandas, Pyspark, Scikit Learn, Machine Learning Operations, Databricks
- **Published:** July 23, 2026
- **Apply:** https://careers.stellantis.com/job/23623388/machine-learning-engineer-auburn-hills-mi/

## About the Role

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

Preferred Qualifications:

 * 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

## Description

We are looking for a Machine Learning Engineer / Data Scientist to develop advanced statistical models and simulations that drive Vehicle Configuration Optimization (VCO). This role will leverage curated datasets to build a customer-level preference simulation engine, enabling optimized vehicle order guides (VOGs) for future model years., * Build and run large-scale simulations (e.g., 50,000 synthetic customers) to model vehicle purchase behavior

 * Develop statistical and machine learning models using Databricks
 * Leverage datasets including:

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

Translate model outputs into optimized Vehicle Order Guides (VOGs) that inform product configuration decisions

Perform exploratory data analysis and feature engineering on complex datasets

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


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