Sr Staff Data Scientist

Stellantis • Auburn Hills, MI

9h

About The Position

The Machine Learning & AI Engineering Team is looking for a Sr Staff Data
Scientist to act as a technical thought leader and architect across quality,
engineering, and vehicle data domains. This role owns the design, evolution, and
application of advanced ML, AI, and experimentation systems that directly
influence product quality, customer experience, and measurable business
outcomes. This is a senior individual contributor role with enterprise-wide
influence, expected to shape strategy, mentor others, and partner closely with
engineering, product, and leadership

Priorities can change in a fast-paced environment like ours, so this role
includes, but is not limited to the following responsibilities:

Technical Leadership & Strategy:
Being the trusted expert who own and evolve the ML & AI framework supporting
quality and engineering products across the organization.
Set technical direction for modeling, experimentation, and data architecture
aligned to business and product strategy
Serve as a trusted advisor to senior stakeholders on ML/AI feasibility,
tradeoffs, and impact

Advanced Analytics & Modeling:
Lead development of predictive, prescriptive, and causal models using
vehicle, IoT, and enterprise data.
Apply advanced statistical, ML, and deep learning techniques to root cause
analysis, quality improvement, and feature optimization.
Design and refine LLM-based and agentic systems for engineering and quality
applications.

Data & Platform Architecture:
Architect and guide implementation of scalable data pipelines and distributed
analytics systems (Spark-based).
Lead model lifecycle management, validation, and performance governance in
production environments.
Ensure solutions are robust, explainable, and suitable for regulated
automotive contexts.

Experimentation & Product Impact:
Lead the experimentation platform and methodology, enabling safe, agile
testing of software and vehicle features.
Translate experimental results into actionable product and engineering
decisions.
Drive measurable outcomes in revenue, warranty cost reduction, and customer
experience.

Knowledge Sharing & Influence:
Democratize learning through contributions to a centralized internal
knowledge base and external technical blog.
Mentor senior and mid-level data scientists; raise the overall technical bar
of the organization.
Educate partners on problem formulation, research design, and interpretation
of results.

Requirements

• Master or PhD Degree required with technical focus (e.g. Data science, Statistics, CS, Physics, Engineering, etc.)
• 8+ years of total experience in data-oriented advanced analytics/ machine learning
• 5+ years of intensive experience on Databricks, Palantir, Snowflake or AWS SageMaker.
• Expert-level proficiency in Python (or R) and SQL for feature engineering and modeling.
• Deep knowledge of statistical methods, ML algorithms, and neural network–based systems.
• Experience designing solutions on distributed data processing platforms (Spark).

Nice To Haves

• Expertise in LLM fine-tuning, agentic systems, or ML systems for engineering use cases
• QA Knowledge for vehicle, propulsion and battery components

Responsibilities

• Being the trusted expert who own and evolve the ML & AI framework supporting quality and engineering products across the organization.
• Set technical direction for modeling, experimentation, and data architecture aligned to business and product strategy
• Serve as a trusted advisor to senior stakeholders on ML/AI feasibility, tradeoffs, and impact
• Lead development of predictive, prescriptive, and causal models using vehicle, IoT, and enterprise data.
• Apply advanced statistical, ML, and deep learning techniques to root cause analysis, quality improvement, and feature optimization.
• Design and refine LLM-based and agentic systems for engineering and quality applications.
• Architect and guide implementation of scalable data pipelines and distributed analytics systems (Spark-based).
• Lead model lifecycle management, validation, and performance governance in production environments.
• Ensure solutions are robust, explainable, and suitable for regulated automotive contexts.
• Lead the experimentation platform and methodology, enabling safe, agile testing of software and vehicle features.
• Translate experimental results into actionable product and engineering decisions.
• Drive measurable outcomes in revenue, warranty cost reduction, and customer experience.
• Democratize learning through contributions to a centralized internal knowledge base and external technical blog.
• Mentor senior and mid-level data scientists; raise the overall technical bar of the organization.
• Educate partners on problem formulation, research design, and interpretation of results.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level