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

About the role

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.

Technical Leadership & ML Strategy (Staff-Level Ownership)

• 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
• Data science architecture
• Act as a trusted technical advisor to senior leadership on:
• Model feasibility
• Trade-offs (accuracy, scalability, cost, interpretability)
• Business impact of ML/AI initiatives
• Influence roadmap decisions across engineering and product organizations

Advanced Machine Learning & Statistical Modeling

• Develop and deploy predictive, prescriptive, and causal models using:
• Vehicle data
• IoT sensor data
• Enterprise datasets
• Apply advanced techniques including:
• Statistical modeling
• Machine learning algorithms
• 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

Data Science Platform & Scalable Systems

• 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
• Maintenance in production
• Ensure models and systems are:
• Explainable
• Reliable
• Production-ready
• Compliant with automotive/regulatory standards

Experimentation & Product Impact

• 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
• Revenue-impacting insights

Influence, Mentorship & Knowledge Sharing

• Mentor senior and mid-level data scientists, raising technical standards across the team
• Help teams with:
• Problem formulation
• Research design
• 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