Drweng

Drweng

Leadership Rotation Network Intern

Chicago, IL · Intern · Internship

Sponsorship not specifiedDetected 9 days ago
PythonGitSQLPandasProject ManagementExcelProcess ImprovementLeadershipCommunicationProblem SolvingCritical ThinkingMentoring

About the role

  • Our formula for success is to hire exceptional people, encourage their ideas and reward their results.
  • Over the course of your 10-week internship, you will contribute meaningfully to your team's objectives and gain in-depth knowledge in your specific area of placement.
  • DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world.

Responsibilities

  • You'll build a professional relationship with an experienced mentor in your field.
  • In addition, throughout the summer, you'll have the opportunity to engage in a speaker series designed to further your understanding of DRW's scope and operations.
  • We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk.

Requirements

  • A Bachelor's or Master's degree in Business, Finance, Business Analytics, Economics, or a related field, with a graduation date between December 2027 and June 2028
  • The ability to work independently while fostering strong working relationships across various business groups

Nice to have

  • Experience with pandas, handling CSV/JSON files, API interaction (requests), and writing scripts/classes.
  • Knowledge of conda/virtualenv, GitHub, and matplotlib/seaborn is a plus.
  • Ability to write intermediate queries (JOINs, CTEs, PIVOT) for data transformation.
  • Familiarity with integrating SQL in analytics/reporting tools is a plus.

Compensation

  • The annual base salary for this position is $90,000.

Visa & Work Authorization

  • Work authorization in the United States without the need for sponsorship

This listing is sourced directly from Drweng's careers page and normalized into a canonical job model.