Senior Staff Data Scientist Jobs
Senior Staff Data Scientist jobs are open across technology, finance, healthcare, and retail, from senior individual contributor to staff and principal levels, with specializations in machine learning, causal inference, and experimentation platforms. Find a role that fits from the openings below and apply directly.
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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
REQUIREMENTS
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
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
REQUIREMENTS
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
See All 39+ Senior Staff Data Scientist Jobs
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Find JobsSenior Staff Data Scientist Job Market
A snapshot from current openings nationwide, updated as new roles post.
Who's Hiring
- Intuit8
- Snorkel AI5
- Google4
- St. Jude Children's Research Hospital3
- Gallup2
Top Industries Hiring
- Technology & Software24
- Artificial Intelligence8
- Healthcare & Medical Services4
- Consulting & Professional Services3
- Electronics & Hardware2
What Employers Look For
The qualifications that appear most often in senior staff data scientist jobs.
- PhD or MS in statistics, computer science, or a related quantitative field with 8 or more years of industry experience
- Deep proficiency in Python and SQL with production-level experience in machine learning frameworks such as PyTorch or TensorFlow
- Experience designing and analyzing large-scale A/B tests and causal inference studies in high-traffic environments
- Demonstrated ability to lead technical strategy and mentor senior data scientists across multiple project teams
- Hands-on experience with distributed computing platforms such as Spark, Databricks, or equivalent cloud-native ML infrastructure
- Strong communication skills with a history of presenting modeling recommendations to executive or cross-functional stakeholders
Tips for Your Senior Staff Data Scientist Job Search
Quantify impact at the systems level
Senior staff roles expect you to show influence beyond individual models. Reframe resume bullets around decisions you shaped, pipelines you designed for reuse, and measurable downstream outcomes, not just model accuracy or experiment volume.
Distinguish staff from senior on your resume
Hiring managers screen hard for cross-functional scope. Explicitly name the teams you partnered with, the roadmap decisions you influenced, and any mentorship or technical direction you provided to other scientists or analysts.
Target job listings by platform and stack
Filter openings by the specific ML infrastructure stack listed in each posting. If you have deep experience in a particular ecosystem, prioritize roles where that stack appears in requirements, not just in the nice-to-have section.
Apply early to roles that fit
Migrate Mate lists senior staff data scientist openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Prepare a technical narrative for the system design round
Staff-level interviews almost always include a machine learning system design session. Practice explaining the full lifecycle of a production system you owned, covering data ingestion, feature engineering, model serving, and monitoring decisions you made and why.
Negotiate scope before you negotiate compensation
At the staff level, job scope varies widely between companies using the same title. Before accepting an offer, clarify reporting structure, team size, and whether the role owns a product area or consults across teams, then negotiate from there.
Senior Staff Data Scientist Jobs: Frequently Asked Questions
Which companies are hiring the most senior staff data scientists?
The companies hiring the most senior staff data scientists right now include Intuit, Snorkel AI, and Google, with the largest share of openings in California, Tennessee, and Georgia, based on current listings on Migrate Mate as of June 2026. Demand is particularly concentrated in companies running large-scale personalization, recommendation, or forecasting systems.
How many senior staff data scientist jobs are remote?
About 33% of senior staff data scientist openings are fully remote or hybrid as of June 2026, making it one of the more location-flexible senior technical roles. Positions focused on experimentation platforms, ML platform engineering, and applied research tend to offer remote options most frequently compared to embedded product science roles.
How do you become a senior staff data scientist?
You become a senior staff data scientist by progressing through individual contributor data science roles while taking on increasing cross-functional scope. Build a record of owning production ML systems end to end, leading technical decisions that affect multiple teams, and mentoring other scientists. Most people reach this level after several years as a senior or staff data scientist at a company where they drove measurable product or business outcomes.
Can you get hired as a senior staff data scientist without prior staff-level experience?
You can get hired at the senior staff level without a prior staff title if your project history demonstrates equivalent scope. Focus your application materials on systems you owned independently, decisions you made without managerial direction, and the scale of the data or traffic those systems handled. Some companies promote strong senior data scientists directly into staff roles after a significant cross-functional project, so internal moves are also a realistic path.
What does the senior staff data scientist interview process look like?
The interview process for a senior staff data scientist typically includes a recruiter screen, a technical phone interview covering statistics or ML fundamentals, a machine learning system design session, a coding or analytical problem-solving round, and a set of cross-functional behavioral interviews focused on leadership and influence. Many companies also include a presentation of past work where you walk through a project you owned, the decisions you made, and the outcome.
Where can I find and apply to senior staff data scientist jobs?
You can find and apply to senior staff data scientist jobs on Migrate Mate, which lists current openings from across the United States. Search the listings to find roles that match your background and apply directly to each one that fits.
See All 39+ Senior Staff Data Scientist Jobs
Jump back to the full list of openings and apply to any senior staff data scientist role that fits.
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