About this role
Data Scientist is a full-time hybrid position at DoorDash ANZ located in Melbourne, VIC or Sydney, NSW, suited for professionals with a quantitative background looking to partner with executive leadership on merchant growth strategies. This role focuses on analyzing data to help merchants operate, grow, and monetize their businesses across the Australia and New Zealand markets.
Role Overview
The Data Scientist position sits within the global BizOps Analytics team at DoorDash ANZ. The professional in this role collaborates directly with the Strategy and Operations group and the ANZ Executive Leadership Team. Responsibilities include identifying local merchant opportunities concerning ads, promos, menu quality, and availability. This hybrid position requires working three days in the office and two days remotely.
Key Responsibilities
Requirements
Skills and Tools
About the Company
DoorDash is a technology and logistics company focused on empowering local economies through door-to-door delivery and expanding into broader goods delivery. The organization values diverse teams from various backgrounds and supports employee well-being through comprehensive benefits and perks.
How to Apply
Interested candidates should apply directly on the official DoorDash careers page.
Skills and tech stack
Questions candidates ask
What is the work arrangement for this role?
This is a hybrid position requiring three days in the office and two days working remotely.
Where is the position located?
The job is located in Melbourne, VIC and Sydney, NSW.
What level of experience is required?
Candidates must have two or more years of experience in data analytics, consulting, or related quantitative roles.
What educational background is needed?
A degree in Math, Physics, Statistics, Economics, Computer Science, or a similar domain is required.
What technical skills and programming languages are expected?
The role requires proficiency in SQL queries, basic ETL, regression techniques, experimentation methods like A/B testing and causal inference, and at least one programming language such as Python or R.