Interested in this Data Scientist role at Michelin?
Apply Now →Skills & Technologies
About This Role
Data Scientist Build a Career That Matters with One of the World’s Most Respected Employers!
- - - - - - - - - - - -
This opportunity is in Woodburn, Indiana in our BF Goodrich tire plant. Woodburn is a small town located about 30 minutes east of Fort Wayne, IN. Fort Wayne is the cultural and economic center of northeastern Indiana and the second largest city in Indiana. Established in 1961, our plant makes passenger and light truck tires.
THE OPPORTUNITY
Michelin has an immediate opening for a Data Scientist who will manage and analyze across disparate datasets to address business questions. Michelin’s purpose is to support everyone is right to move freely to find their better way forward. Our continuous improvement stimulates innovation and allows us to manufacture locally, which in turn, strengthens the local communities. Entry level training is provided by Michelin and will include formal classes and on -the-job mentoring under an expert area guide. If respect for people, teamwork and trust are some values you live by you should consider joining us, the Worldwide leader in Tires!
WHAT WILL YOU DO
- Data Management, overseeing the collection, storage, and interpretation of data for our business.
- Analysis & Reporting, sifting through data points to create organized categories, comparing them to current company processes, and writing reports to outline business predictions or proposals.
- Problem-Solving, using data to help the company make decisions, finding patterns and solutions to improve our capabilities, and develop investment strategies.
- Technical Skills, utilizing statistical analysis, machine learning, and programming skills to find new ways to collect and analyze data, to find long-term challenges for the company.
- Understanding customer needs (level=easy or medium) form RDI, Manufacturing, Marketing, etc.
- Determining the relevant data among those available and identifying the missing data to acquire
- Consolidating/Aggregating the finally selected data.
- Analyzing and building the models, including predictive models
- Presenting in detail the results and/or the model (performances and limits) so that the final customer can use it or upgrade the results with the future available data.
- Building and automating all steps of Machine Learning industrialization workflow
- Promoting the AI models to production by working closely with data and platform engineers.
WHAT WILL YOU BRING
- Analytical skills to identify problems, research strategies and recommend effective solutions.
- Creativity to pursue new ways of thinking and to design innovative solutions.
- Programming languages to develop algorithms and create applications, using languages like Python, R, Java, Hadoop/spark, Tableau/Power BI, and SQL
- Math and statistics to analyze and model data.
- Data visualization to help create models to represent data and present that data to interested stakeholders.
- Machine learning to create artificial intelligence that can learn based on patterns.
- Data mining, being able to turn raw data into useful information.
- Demonstrated experience in leading a team or project group by setting clear goals, delegating tasks effectively, providing regular feedback/coaching, leading by example, and fostering a positive work environment.
- Ability to influence and negotiate with others both I verbal and written forms.
- Ability to set and achieve goals with minimum supervision.
- Good interpersonal skills to show appreciation, use empathy to understand, resolve disputes professionally, and to practice active listening skills.
- Developed and implemented project plans that met or exceeded expectations.
- Bachelor’s Degree inData science required.
- Master’s Degree in Machine learning or Big data analytics preferred.
#LI-SB1
Ready to Shape the Future of Innovation?
Michelin is building a world-leading manufacturer of life-changing composites and experiences. Pioneering engineered materials for more than 130 years, Michelin is uniquely positioned to make decisive contributions to human progress and a more sustainable world. Drawing on its deep know-how in polymer composite materials, Michelin is constantly innovating to manufacture high-quality tires and components for critical applications in demanding fields as varied as mobility, construction, aeronautics, low-carbon energies and healthcare.
The care placed in its products and deep customer knowledge inspire Michelin to offer the finest experiences. This spans from providing data- and AI-based connected solutions for professional fleets to recommending outstanding restaurants and hotels curated by the MICHELIN Guide.
Why Michelin?
- Career Growth: Personalized development plans, mentorship, and cross-functional opportunities. Unique career paths and opportunities for advancement.
- Inclusive Culture: Thrive in a diverse, supportive environment where your competencies, contributions and behaviors are recognized. Option to join one of our Connected Communities.
- Innovation-Driven: Work on projects that matter—from sustainable materials to digital transformation.
- Community Impact: Be part of a company that does what's right. We use sustainable business practices while balancing the needs of our customers and communities.
Michelin provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, sex (including pregnancy, gender identity, and sexual orientation), parental status, national origin, age, disability, genetic information (including family medical history), political affiliation, military service, or other non-merit-based factors. Consistent with these obligations, Michelin also provides reasonable accommodations to employees and applicants with disabilities and for sincerely held religious beliefs. If you need accommodation for any part of the employment process because of a disability, please contact us at [email protected].
This position is not available for immigration sponsorship.
We build the future with people like you. Begin your career with Michelin today!
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 11,520 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Michelin, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,564 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Michelin AI Hiring
Michelin has 1 open AI role right now. They're hiring across Data Scientist. Based in Woodburn, IN, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.