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About This Role
Who We Are
We’re Dealer Tire, a family-owned, international distributor of tires and parts established in 1918 in Cleveland, OH. We’re laser focused on helping the world’s largest and most trusted auto manufacturers grow their tire business—in fact, we’ve sold more than 60 million tires to date. We’re a thriving company, and we’re looking for driven individuals to join our team. That’s where you come in!
Base Pay Range:
$80,580 - $110,000As an Associate Data Scientist, your essential job functions will include the following:
Perform Advanced Modeling & Analysis
- Execute assigned data science projects with defined scope, delivering accurate and timely results
- Develop and implement statistical and machine learning models under guidance, including regression, classification, clustering and forecasting techniques
- Apply appropriate analytical methods based on provided problem definitions, datasets and evaluation criteria
- Utilize interactive development environments (e.g., notebooks) to explore data, prototype models and validate approaches
- Interpret model outputs and assist in translating findings into actionable insights for stakeholders
- Leverage AI-assisted development tools to accelerate analysis, research and code development
Perform AI Engineering
- Utilize AI tools and large language models (LLMs) to support analysis, code generation and research tasks
- Apply basic prompt engineering techniques to improve outputs from AI-assisted workflows
- Assist in building simple AI-enabled workflows or components under guidance
- Develop familiarity with emerging AI techniques and their application within data science workflows
Contribute toData Science Products
- Contribute to the development and maintenance of data science products, including models, pipelines, reporting
- Support implementation of model training, evaluation and output generation workflows
- Assist in documenting data science processes, model assumptions and system behavior
- Generate regular reporting and analysis to support performance monitoring of data science products
- Communicate progress and results to team members and stakeholders as needed
Manage Large Datasets – Cleaning, Transforming, Understanding
- Clean, transform and prepare large structured and unstructured datasets for analysis and modeling
- Write SQL and Python-based transformations to create usable datasets from raw data sources
- Work with Snowflake and dbt workflows to support development of analytics-ready datasets
- Assist in developing and maintaining data pipelines used in modeling workflows
- Perform exploratory data analysis to understand data characteristics, distributions and potential issues
Data Science Quality and Governance
- Follow established software development best practices, including version control, documentation and testing standards
- Write clear, maintainable and testable code for data science workflows
- Assist in validating datasets, model outputs and reporting results for accuracy and consistency
- Participate in code reviews, focusing on syntax correctness and general structure
- Ensure reproducibility of analyses through proper documentation and organization of work
Technology and TechnicalDevelopment
- Develop proficiency in Python as the primary programming language and use R where appropriate for statistical analysis
- Utilize standard data science libraries and frameworks (e.g., scikit-learn, pandas, numpy)
- Begin developing modular code practices and reusable functions
- Gain familiarity with containerization (e.g., Docker) and deployment concepts
- Use AI-assisted tools to improve development speed and code quality
ThoughtDevelopment and Collaboration
- Build foundational business acumen by understanding how data science outputs support business decision-making
- Communicate findings and progress clearly with team members and stakeholders
- Seek feedback and continuously improve technical and analytical skills
- Collaborate effectively with other Data Scientists, Analytics Engineers and business partners
Build Trust and Reputation for Data Science
- Deliver accurate, reliable and well-documented analytical outputs
- Support team members by providing consistent, high-quality work
- Develop expertise in assigned datasets, tools and processes
- Build strong working relationships within the Data Science team and with cross-functional partners
Other Duties as Assigned
Position Requirements
- Bachelor’s degree in Business, Mathematics, Computer Science, Engineering, or related field
- Advanced degree in a quantitative field preferred
- 0–2 years of experience in data science, analytics, or related roles -OR- advanced degree
- Ability to work in a fast-paced environment with a strong sense of urgency
- Strong intellectual curiosity and desire to learn
Technical Skills
- Strong foundational programming skills in Python; familiarity with R is a plus
- Basic understanding of statistical modeling and machine learning concepts
- Proficiency in SQL and experience working with relational data systems
- Familiarity with Snowflake, dbt, or modern data platforms is a plus
- Exposure to version control practices (Git) and development workflows
- Familiarity with data science libraries (e.g., scikit-learn, pandas) is a plus
- Exposure to AI tools and prompt engineering concepts is a plus
Competencies Required
- Critical Thinking
- Agility
- Initiative
- Attention to Detail
- Willingness to Learn
Physical Job Requirements
- Continuous viewing from and inputting to a computer screen
- Sitting for long periods of time
- Travel as necessary, <10%
Drug Policy
Dealer Tire is a drug-free environment. All applicants being considered for employment must pass a pre-employment drug screening before beginning work.
This position is targeted to be closed on:
2026-09-19 Why Dealer Tire: An amazing opportunity to join a growing organization, built on the efforts of hard working, innovative, and team-oriented people. The compensation offered for this position will depend on qualifications, experience, and geographic location. The total compensation package may also include commission, bonus or profit sharing. Sign on bonus is subject to state and local laws, terms and conditions, including employment status and length-of-service requirements. We offer a competitive & comprehensive benefit package including: paid time off, medical, dental, vision, and 401k match (50% on the dollar up to 7% of employee contribution). For more information on our benefit offerings, please visit our Dealer Tire Family of Companies Benefits Highlights Booklet.
EOE Statement: Dealer Tire is an Equal Employment Opportunity (EEO) employer and does not discriminate on the basis of race, color, national origin, religion, gender, age, veteran status, political affiliation, sexual orientation, marital status or disability (in compliance with the Americans with Disabilities Act*), or any other legally protected status, with respect to employment opportunities.
- ADA Disclosure: Any candidate who feels that they may need an accommodation to complete this application, or any portions of same, based on the impact of a disability should contact Dealer Tire’s Human Resources Department to discuss your specific needs. Please feel free to contact us at 1-800-933-2537 x6550.
Salary Context
This $80K-$110K range is in the lower quartile for Data Scientist roles in our dataset (median: $161K across 138 roles with salary data).
View full Data Scientist salary data →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 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Dealer Tire, 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 1,503 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $111,000. This role's midpoint ($95K) sits 50% below the category median. Disclosed range: $80K to $110K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Dealer Tire AI Hiring
Dealer Tire has 1 open AI role right now. They're hiring across Data Scientist. Based in Cleveland, OH, US. Compensation range: $110K - $110K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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
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