Data Scientist

Stellantis•Auburn Hills, MI

16h

About The Position

The Machine Learning & AI Engineering team is looking for a Data Scientist to independently deliver advanced analytics and machine learning solutions across quality and engineering domains. This role focuses on strong technical execution, applied modeling, and cross functional collaboration to solve complex quality and engineering problems using established ML and AI approaches. The data scientist contributes to shared frameworks, experimentation practices, and reusable assets, while operating under the broader technical direction set by senior staff and internal stakeholders.

Requirements

Bachelor Degree required with technical focus (e.g. Data science, Statistics, CS, Physics, Engineering, etc.)
Minimum of 2 years of total experience in data-oriented advanced analytics/ machine learning
Exceptional technical skills with Python/R, SQL for data analysis and experience in ML driven products
Knowledge of advanced statistical and ML algorithms, experience in Root Cause Analysis is desirable
Expert at deriving narrative from data and communicate the results effectively

Nice To Haves

Comprehensive experience with predictive and prescriptive modeling/ machine learning
Understanding of ASPICE or similar development lifecycle standards.
Experience in automotive, manufacturing, or quality focused analytics environments.

Responsibilities

Design, develop, and deploy predictive and statistical models using vehicle and enterprise data.
Apply advanced analytics and ML techniques to root cause analysis, quality improvement, and feature optimization.
Support development of LLM based or ML driven solutions for engineering and quality use cases, leveraging existing architectures and patterns.
Contribute to the evolution of shared ML frameworks, tooling, and best practices.
Ensure models and analytics solutions are robust, explainable, and suitable for regulated automotive contexts.
Support experimentation methodologies and platforms to enable safe and effective testing of software and product features.
Analyze experimental results and translate findings into actionable recommendations for engineering and product teams.
Work closely with engineering, quality, and product partners to translate business problems into data science solutions.
Clearly communicate analytical findings to both technical and non technical audiences.
Document methodologies and contribute to centralized knowledge repositories.