Machine Learning Engineer

Stellantis

Auburn Hills, USonsitePosted Jul 22, 2026
Posting intelligenceActively listedReposted 9×, possible evergreen/ghost posting

Skills

scikitlearndatabrickspandaspythonsparknumpy

About the role

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

Requirements:

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

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