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Machine Learning Engineer

Stellantis
7 hours ago
Full-time
On-site
Auburn Hills, Michigan, United States
ML & AI Engineering

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.

This is a highly impactful role for an early-to-mid career data scientist who enjoys combining statistical rigor, large-scale data, and real-world business impact.

Key Responsibilities:

  • 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
Qualifications

Basic Qualifications:

  • 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