| Location | Auburn Hills, MI |
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
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