Data Scientist

Stellantis•Auburn Hills, MI

About The Position

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.

Requirements

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

Nice To Haves

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

Responsibilities

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