Logistics Data Analyst Intern Jobs in Arizona (NOW HIRING)

The Data Science Analyst is responsible for using data science, machine learning, statistical modeling, and advanced analytics to solve complex business problems across Supply Chain and Operations. This role works at the intersection of data, technology, and business operations to develop predictive insights, analytical models, and decision-support tools that improve planning, efficiency, cost management, and service performance.

The Data Science Analyst partners with business leaders, operations teams, IT, data engineering, and analytics stakeholders to identify high-value use cases, build and test analytical models, and translate technical outputs into actionable business recommendations. This role is well suited for a candidate who has strong technical skills but also enjoys applying those skills to practical operational challenges.

• Develop, test, and validate predictive models and machine learning solutions for Supply Chain and Operations use cases.
• Apply statistical modeling, forecasting, classification, regression, clustering, optimization, and other data science techniques to solve business problems.
• Support use cases such as demand forecasting, labor forecasting, inventory risk, transportation optimization, order volume prediction, customer behavior trends, productivity analysis, and operational exception detection.
• Develop and deploy anomaly detection models to identify operational exceptions, service disruptions, inventory irregularities, equipment failures, process deviations, and emerging business risks before they impact performance.
• Support transportation and logistics optimization initiatives by applying advanced analytics and machine learning techniques to improve route efficiency, reduce fuel consumption, optimize network flows, and enhance service performance.
• Clean, transform, structure, and analyze large datasets from multiple business systems.
• Perform exploratory data analysis to identify relationships, trends, anomalies, and improvement opportunities.
• Collaborate with business stakeholders to understand operational processes, pain points, and decision-making needs.
• Translate business problems into data science questions, analytical methods, and measurable outcomes.
• Evaluate model accuracy, performance, stability, and business value.
• Partners with data engineering and IT teams to access, prepare, and improve data sources needed for modeling and analysis.
• Create dashboards, visualizations, and presentations to communicate model outputs and recommendations.
• Document model logic, assumptions, data sources, limitations, and business applications.
• Support the deployment, monitoring, and ongoing refinement of analytical and machine learning solutions.
• Stay current on data science, machine learning, AI, and analytics methods that may benefit the business.
• Other duties as assigned.

• Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Business Analytics, Economics, Operations Research, Supply Chain, or a related field required.
• Master’s degree in Data Science, Analytics, Statistics, Operations Research, Computer Science, Industrial Engineering, or related discipline preferred.
• 2-5 years of experience in data science, analytics, statistical modeling, machine learning, business intelligence, or related analytical roles.
• Experience applying data science methods to real-world business or operational problems.
• Experience building predictive models, forecasts, or machine learning prototypes preferred.
• Proficiency in Python, R, SQL, or similar analytical programming languages.
• Familiarity with machine learning libraries and methods such as scikit-learn, pandas, NumPy, regression models, classification models, clustering, time series forecasting, or optimization techniques.
• Strong understanding of statistics, probability, data modeling, feature engineering, and model evaluation.
• Ability to work with structured and unstructured data from multiple systems.
• Experience with Power BI, Tableau, Databricks, Azure Machine Learning, Snowflake, or similar platforms preferred.
• Ability to explain technical concepts to non-technical business stakeholders.
• Strong problem-solving, critical thinking, and analytical reasoning skills.
• Strong communication and data storytelling skills.
• Ability to balance technical depth with practical business applications.
• Must be flexible and willing to work the demands of the department which is generally limited to weekdays but may be subject to evenings or weekends due to project or department needs.

At Shamrock Foods Company, people come first – our associates, our customers, and the families we serve across the nation. A privately-held, family-owned and -operated Forbes 500 company, Shamrock is an innovator in the food industry and has been since being founded in Arizona in 1922.

At Shamrock Foods Company, we live by our founding family’s motto to "treat associates like family and customers like friends."

• Benefits are a major part of your overall compensation, and we believe offering them at an affordable cost is not only the right thing to do, but it helps keep you and your family healthy.
• That’s why Shamrock Foods pays for the majority of your health insurance, allowing you to take home more of your paycheck.
• And it doesn’t stop there - our associates also enjoy additional benefits such as 401(k) Savings Plan, Profit Sharing, Paid Time Off, as well as our incredible growth opportunities, continued education, and wellness programs.

At Shamrock Foods Co all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, veteran status, sexual orientation, gender identity or any other basis protected by applicable law.

Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
This employer is required to notify all applicants of their rights pursuant to federal employment laws.For further information, please review the Know Your Rights notice from the Department of Labor.