Sr. Staff Data Scientist – Machine Learning & AI (Quality, Vehicle & Engineering Analytics)

Stellantis•Auburn Hills, MI

About The Position

We are looking for a Senior Staff Data Scientist (ML/AI) to serve as a technical leader, architect, and individual contributor within the Machine Learning & AI Engineering team at Stellantis. This role sits at the intersection of machine learning, advanced analytics, experimentation, and large-scale vehicle/IoT data systems. You will define and influence how ML and AI are used across vehicle quality, engineering systems, and customer experience outcomes. This is a high-impact, senior IC role (Staff/Principal level influence) responsible for shaping technical strategy, designing scalable ML systems, and driving measurable business outcomes such as quality improvement, warranty reduction, and customer experience enhancement.

Requirements

Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field
A minimum of 8 years of experience in data science, advanced analytics, or machine learning
A minimum of 5 years of hands-on experience with Databricks, Palantir, Snowflake, or AWS SageMaker
Expert-level proficiency in Python (or R)
Expert-level proficiency in SQL
Strong foundation in machine learning algorithms
Strong foundation in statistical modeling
Strong foundation in neural networks / deep learning
Experience building ML solutions on distributed systems (e.g., Spark)

Nice To Haves

Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field
Experience with Large Language Models (LLMs)
Experience with fine-tuning foundation models
Experience with Agentic AI systems
Experience building ML solutions in engineering, automotive, propulsion, or battery systems
Strong understanding of vehicle quality (QA), reliability, or manufacturing analytics
Experience working in high-scale enterprise or regulated environments

Responsibilities

Define and evolve the ML/AI architecture and framework supporting quality, engineering, and vehicle analytics across the organization
Set technical direction for machine learning systems, experimentation platforms, and data science architecture
Act as a trusted technical advisor to senior leadership on model feasibility, trade-offs (accuracy, scalability, cost, interpretability), and business impact of ML/AI initiatives
Influence roadmap decisions across engineering and product organizations
Develop and deploy predictive, prescriptive, and causal models using vehicle data, IoT sensor data, and enterprise datasets
Apply advanced techniques including statistical modeling, machine learning algorithms, and deep learning / neural networks
Lead root cause analysis for vehicle quality, performance, and system failures
Design and build LLM-based systems and agentic AI solutions for engineering and quality use cases
Architect and guide development of large-scale distributed data and ML systems
Build and scale analytics pipelines using Spark-based distributed processing frameworks
Lead ML model lifecycle management, including training, validation, deployment, and monitoring in production
Ensure models and systems are explainable, reliable, production-ready, and compliant with automotive/regulatory standards
Own and evolve the experimentation framework/platform for safe, scalable testing of vehicle and software features
Design statistically sound experiments (A/B tests and beyond)
Translate experimental results into clear product and engineering decisions
Drive measurable business outcomes including warranty cost reduction, improved product quality, enhanced customer experience, and revenue-impacting insights
Mentor senior and mid-level data scientists, raising technical standards across the team
Help teams with problem formulation, research design, and statistical interpretation
Contribute to internal knowledge systems and external-facing technical content (e.g., blogs or papers)
Serve as a cross-functional leader bridging engineering, product, and executive teams