Data Scientist

Stellantis; Auburn Hills (MI); On-site; USD 120,000 - 180,000; Full time

Job summary

Stellantis seeks a Data Scientist to build trusted analytics products powering marketing performance measurement. You will collaborate across teams, design econometric models for incentives, pricing and demand, and develop scalable predictive models with Python and big data tools. Strong storytelling and production readiness are essential.

You will also quantify impact, interpret elasticities, and help drive data-driven decisions for sales and margins across regions and brands.

Qualifications

• Bachelor's degree in Statistics, Economics or related quantitative field.
• 5+ years in data science, econometrics or related area.
• Proficiency in Python and SQL.
• Hands-on with big data and cloud platforms (Databricks, Snowflake, Spark).
• Experience with MLOps practices, versioning, monitoring, pipelines.
• Knowledge of regression, causal models and experimental design.
• Ability to translate complex data into actionable insights for business stakeholders.

Responsibilities

• Collaborate with stakeholders to identify high-impact analytics opportunities.
• Design and implement econometric and causal inference models for pricing and incentives.
• Estimate price elasticities across brands, segments and regions.
• Develop and validate predictive models using regression, forests, boosting and neural nets.
• Document methodologies and ensure model quality for executive review.
• Communicate results clearly to technical and non-technical audiences.
• Work with data engineers to source features and support model deployment.
• Maintain models in production ensuring scalability and performance.
• Support KPI measurement and cross-functional data initiatives.

Skills

Python

SQL

ML algorithms

Experiment design

Data storytelling

Statistical inference

Econometrics

CI/CD for models

Education

Bachelor's degree in quantitative field

Master's degree preferred

Tools

Databricks

Snowflake

Spark / PySpark

Power BI

Job description

The Commercial Analytics team is looking for a Data Scientist to join our team. Your mission is to build and scale trusted data science products that power marketing performance measurement while promoting data science best practices, actionable recommendations and a high bar for model quality and reliability.

Data scientists work closely with data engineers, analysts, and business teams to design analytics solutions, implement advanced algorithms and evaluate the performance of use cases. Ideal candidates are self-motivated, inquisitive and creative, with a strong desire to solve real-world problems using data.

In this role, you will:

• Collaborate with business stakeholders to identify high-impact opportunities for statistical and machine learning use cases
• Design and implement econometric and causal inference models to quantify the impact of vehicle incentives, pricing, and commercial levers on sales, margin, and demand
• Estimate and interpret price and incentive elasticities across brands, segments, and regions, informing pricing and go-to-market strategies
• Develop defensible, well-documented methodologies that stand up to executive scrutiny and support strategic decision-making
• Communicate complex results clearly to both technical and non-technical audiences
• Partner with Data Engineers to define and source relevant data features for modeling as well as drive adoption and a deep understanding of proper data usage
• Develop and validate predictive models using techniques such as regression, random forests, gradient boosting, causal modeling and neural networks
• Communicate findings and recommendations to non-technical audiences through clear visualizations and storytelling
• Contribute to the maintenance of models in production environments, ensuring scalability and performance
• Conduct peer code reviews and support best practices in model development and deployment
• Collaborate with both external and internal resources to support business requirements and key KPI measurement

Basic Qualifications:

• Bachelor's degree in a quantitative discipline (e.g., Statistics, Economics or other quantitative field)
• Minimum of 5 years of experience in data science, econometrics or a related field
• Proficiency in Python and SQL
• Hands-on experience with big data and cloud platforms such as Databricks, Snowflake or Spark
• Exposure to MLOps best practices, including model versioning, monitoring, and deployment pipelines
• Strong grasp of machine learning algorithms like:
  • Regression (linear, logistic)
  • Causal Inference Models (Difference-in Difference, Regression Discontinuity Design)
• Experience with experimental design, and statistical inference
• Ability to translate complex data into actionable insights for business stakeholders

Preferred Qualifications:

• Master's degree in a quantitative discipline (e.g., Statistics, Economics or other quantitative field)
• Automotive experience
• Tree-based models (Random Forest, XGBoost, LightGBM)
• Clustering and dimensionality reduction (e.g., LDA, PCA, Dynamic Time Warping)
• Experience using PySpark for distributed data processing and feature engineering
• Experience with Power BI or similar tools for data visualization and dashboarding
• 2+ years of experience working with finance / pricing / incentives data
• 2+ years of experience working with sales / commercial data
• Strong communication and storytelling skills with the ability to influence decision-makers
• Understanding of CI/CD workflows for automating model testing and deployment
• Experience working with real-time data pipelines and event-driven architectures

At Stellantis, we assess candidates based on qualifications, merit, and business needs. We welcome applications from all people without regard to sex, age, ethnicity, nationality, religion, sexual orientation, disability, or any characteristic protected by law. We believe that diverse teams reflect our identity as a global company, enabling us to better address the evolving needs of our customers and care for our future.