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

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 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:

Python (or R)

SQL

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