Operationscareers

Operationscareers

E-Scooter Delivery Driver - Manhattan

Manhattan, Kansas, United States · Part-time

Sponsorship not specifiedDetected 30 days ago
Communication

About the role

  • Veo seeks operations professionals who are passionate about providing riders with excellent experience by maintaining our vehicles and continually improving the efficiency of our fleet operations.
  • The ideal candidate for this role will be a team player who loves to solve problems, work with their hands, and be active throughout an entire shift.

Responsibilities

  • Drive a company's van to find scooters and swap battery
  • Perform basic repair and quality check

Requirements

  • All candidates must have reliable transportation to the warehouse located on Seth Child Rd, Manhattan, KS.

Skills

  • 21+ years old
  • Valid driver license and acceptable driving record
  • Have a smartphone and be App-savvy
  • Ability to lift up to 60 lbs scooters without assistance
  • Good communication and attention to detail
  • Nice to Haves
  • Knowledge of the local geography and street layout
  • Flexible schedule including early morning, night, and weekend shifts
  • Related experience in warehouse, manufacturing, delivery, etc.

Compensation

  • $14.50 - $15 USD
  • A company van is provided to all employees.
  • All candidates must have reliable transportation to the warehouse located on Seth Child Rd, Manhattan, KS.
  • Company Overview
  • At Veo, our mission is to end car dependency by making clean transportation accessible to all.
  • A leading shared micromobility provider in North America, Veo provides millions of bike and scooter rides annually in over 60 cities and universities from Los Angeles to New York City.
  • Below is the expected salary range for this position.

Benefits

  • Flexible work hours
  • Full time employees are eligible for: Medical/dental/vision coverage, PTO
  • This is a 1099 position and is not eligible for benefits.

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