Supply Chain Analytics Intern: Drive Data-Driven Growth

Wonder Group, INC; United States; On-site; USD 42,000 - 54,000; Part time

Benefits offered by this job

Equity & 401(k)

Health plans

Disability coverage

Paid time off

Meal discounts

Job summary

Wonder, NYC-based, invites a Supply Chain Analytics Intern to learn and shape our evolving supply chain. You’ll analyze data, drive strategic initiatives, and help design key systems that impact the end-to-end operation across restaurants and facilities in the tri-state area.

Ideal candidates are detail-oriented, comfortable with ambiguity, and ready to translate insights into actionable changes with cross-functional teams.

Qualifications

• Current enrollment in a Bachelor's degree program with at least one year remaining.
• Focus in Information Systems, Data Analytics, Computer Science, Business, or related quantitative field preferred.
• Ability to write SQL queries to answer business questions and provide data-driven insights; bonus for CTEs and window functions.
• Experience with data visualization tools like Looker, Tableau, or Power BI.

Responsibilities

• Initiate new ideas, problem-solve using data, and implement strategic solutions with far-reaching influence.
• Conduct on-site investigations to understand real-world impact of initiatives beyond the data.
• Cultivate relationships with stakeholders to drive standard work, alignment, and productivity optimization.
• Provide input to Operations Product in the design and development of key Supply Chain systems and technology platforms.

Skills

SQL queries

Data analysis

Analytical thinking

Education

Bachelor's degree program in Information Systems/Data Analytics/CS/Business

Tools

Looker

Tableau

Power BI

Job description

Wonder, NYC-based, invites a Supply Chain Analytics Intern to learn and shape our evolving supply chain. You’ll analyze data, drive strategic initiatives, and help design key systems that impact the end-to-end operation across restaurants and facilities in the tri-state area.

Ideal candidates are detail-oriented, comfortable with ambiguity, and ready to translate insights into actionable changes with cross-functional teams.