Machine Learning Engineer

Machine Learning Engineer / Data Scientist

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

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