招聘
Tesla is seeking a highly process-oriented, experienced, and self-driven Capacity Planner to support finished vehicle logistics. The capacity planning team is responsible for bringing sufficient trucking coverage daily at two North American factories to ensure smooth and consistent outflow of the volume from the factory. Given that Tesla's factories operate on a 24/7 schedule, maintaining consistent and reliable trucking availability is essential to support the uninterrupted outflow of production volumes. In this pivotal role, you will collaborate closely with internal and external stakeholders to meet daily coverage requirements across both factories. Additionally, you will develop simulation models to evaluate potential scenarios, assess associated risks, and implement targeted process enhancements to optimize logistics performance.
This position demands a proactive individual with a strong analytical mindset, capable of thriving in a fast-paced, high-stakes environment where precision and foresight directly impact Tesla's operational excellence and customer delivery commitments.
What You'll Do
Compensation and Benefits
Along with competitive pay, as a full-time Tesla employee, you are eligible for the following benefits at day 1 of hire:
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关于Tesla

Tesla
PublicA financial leasing taxi company that provides vehicles to customers
140,000+
员工数
Ciudad De Panamá
总部位置
$800B
企业估值
评价
3.8
10条评价
工作生活平衡
2.2
薪酬
3.8
企业文化
3.5
职业发展
4.1
管理层
2.8
65%
推荐给朋友
优点
Innovative projects and cutting-edge technology
Great team and supportive colleagues
Opportunities for growth and learning
缺点
Long hours and poor work-life balance
High pressure and tight deadlines
Management issues and high expectations
薪资范围
1,398个数据点
Junior/L3
Mid/L4
Senior/L5
Junior/L3 · ASSOCIATE MANAGER, LOGISTICS
2份报告
$131,560
年薪总额
基本工资
$114,400
股票
-
奖金
-
$131,560
$131,560
面试经验
4次面试
难度
3.5
/ 5
时长
14-28周
体验
正面 0%
中性 75%
负面 25%
面试流程
1
Application Review
2
Recruiter Screen
3
Technical Phone Screen
4
Take-home Assignment
5
Panel Interview
6
Offer
常见问题
Coding/Algorithm
Technical Knowledge
Behavioral/STAR
System Design
Machine Learning Concepts
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