Job Description
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.
What You Will Do:
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
- Monitoring 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
What Success Looks Like (Top Performers)
Strong candidates will demonstrate:
- Proven impact from deployed ML systems or production analytics products
- Quantifiable improvements in:
- Vehicle quality
- Warranty reduction
- Customer experience metrics
- Ability to influence technical strategy beyond their immediate team
- Strong communication skills with executive and non-technical stakeholders
Demonstrated ability to turn complex analysis into business decisions and outcomes
Qualifications
Basic Qualifications:
- 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, including a minimum of 5 years of hands-on experience with Databricks, Palantir, Snowflake, or AWS SageMaker
- Expert-level proficiency in:
- Strong foundation in:
- Machine learning algorithms
- Statistical modeling
- Neural networks / deep learning
- Experience building ML solutions on distributed systems (e.g., Spark)
Preferred Qualifications:
- Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field
- Experience with:
- Large Language Models (LLMs)
- Fine-tuning foundation models
- 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
