Mentored Data Scientist Intern — NumPy & Hadoop, Madison

Shopping Solutions Plus; Madison (WI); On-site; USD 72,000 - 102,000; Full time

Job summary

Shopping Solutions Plus in Madison, Wisconsin is launching an internship Data Scientist role for an engineer fluent in Leadership and allergic to undocumented surprises. The position offers a $72,000 - $102,000 annual range and a mentorship-focused environment to sharpen Hadoop and NumPy skills.

Responsibilities include refactoring legacy code, building frontend interfaces with Hadoop, owning the NumPy workstream, catching race conditions, and scaling Matplotlib services regionally in Wisconsin.

Qualifications

• NumPy fundamentals plus the Hadoop polish clients notice.
• Familiarity with Shopping Solutions Plus-scale workflows, or the appetite to reach them.
• Demonstrated wins in technology work somewhere near Madison, WI.
• Comfortable presenting ideas to stakeholders at every level.
• A relentlessly curious bias toward action, balanced by knowing when to wait.
• Comfort being measured against a clear mid-level bar.

Responsibilities

• Profile and refactor legacy code to reduce technical debt over time
• Build responsive, accessible front-end interfaces with Hadoop
• Own the mid-level NumPy workstream that unblocks the rest of Shopping Solutions Plus's Madison, WI roadmap
• Catch the NumPy race conditions that only surface under Madison peak traffic
• Pull Shopping Solutions Plus's NumPy stack out of the WI region before the migration deadline
• Scale Shopping Solutions Plus's Matplotlib services from Madison pilot to WI-wide rollout

Skills

Stakeholder presentation

Bias toward action

Leadership

Tools

NumPy

Hadoop

Matplotlib

Job description

Shopping Solutions Plus in Madison, Wisconsin is launching an internship Data Scientist role for an engineer fluent in Leadership and allergic to undocumented surprises. The position offers a $72,000 - $102,000 annual range and a mentorship-focused environment to sharpen Hadoop and NumPy skills.

Responsibilities include refactoring legacy code, building frontend interfaces with Hadoop, owning the NumPy workstream, catching race conditions, and scaling Matplotlib services regionally in Wisconsin.