Associate Data Scientist

Dealer Tire•Remote California, CA

2d•$80,580 - $110,000•Onsite

About The Position

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! As an Associate Data Scientist, your essential job functions will include performing advanced modeling & analysis, AI engineering, contributing to data science products, managing large datasets, ensuring data science quality and governance, and developing technology and technical skills. You will also focus on thought development and collaboration, building trust and reputation for data science, and performing other duties as assigned.

Requirements

Bachelor’s degree in Business, Mathematics, Computer Science, Engineering, or related field
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
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
Exposure to version control practices (Git) and development workflows

Nice To Haves

Advanced degree in a quantitative field preferred
Familiarity with Snowflake, dbt, or modern data platforms is a plus
Familiarity with data science libraries (e.g., scikit-learn, pandas) is a plus
Exposure to AI tools and prompt engineering concepts is a plus

Responsibilities

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
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 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
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
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
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
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
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