Junior Data Scientist

NASCAR•Daytona Beach, FL

About The Position

NASCAR is seeking a Junior Data Scientist based in Daytona Beach, Florida, to transform data into actionable insights and intelligent solutions that support business decisions across the organization. The role involves analyzing data, supporting predictive modeling initiatives, enhancing AI-enabled analytics tools, and collaborating with stakeholders to address business challenges. It offers hands-on exposure to modern analytics, generative AI, and emerging technologies, while contributing to data-driven decision-making at NASCAR. Travel of up to 10% throughout race season is expected.

Requirements

Degree in Data Science, Statistics, Mathematics, Computer Science, Economics, or a related field, or an equivalent combination of education and practical experience.
Foundational SQL skills, including joins, aggregations, and common table expressions.
Experience using Python for data preparation and analysis.
Understanding of core statistical concepts and familiarity with basic machine learning methods and model evaluation.
Foundational understanding of generative AI’s capabilities and limitations, interest in learning MCP and tool calling, and commitment to validating AI-generated results.
Strong analytical, organizational, and communication skills, attention to accuracy, and ability to explain technical findings in business terms.
Collaborative, curious mindset and willingness to learn new tools, ask questions, and manage competing priorities.

Responsibilities

Prepare, combine, and analyze data using SQL and Python; investigate data quality issues and document business definitions.
Apply statistical methods and support predictive modeling to understand fan behavior, identify trends, forecast outcomes, and evaluate business performance.
Help develop, maintain, and enhance AI-enabled analytics tools, including Python-based tools and MCP integrations.
Test analytical tools and AI-generated answers against trusted SQL queries and reports; investigate errors and improve underlying logic, instructions, and tool descriptions.
Collaborate with business partners to translate questions and feedback into analyses, visualizations, and practical tools, communicating findings and limitations clearly.
Work with data engineering and business intelligence colleagues to create repeatable workflows, maintain documentation, and follow established data access and governance practices.
Support user adoption, ongoing analytical needs, and additional projects as assigned.