(Senior) Data Scientist (Analytics), Courier at Wolt
Helsinki, Etelä-Suomi, Finland -
Full Time


Start Date

Immediate

Expiry Date

24 Apr, 25

Salary

0.0

Posted On

25 Jan, 25

Experience

3 year(s) or above

Remote Job

No

Telecommute

No

Sponsor Visa

No

Skills

Good communication skills

Industry

Information Technology/IT

Description

Analytics & Data Science
Helsinki, Finland · Stockholm, Sweden · Berlin, Germany
Full-time
We are looking for a (Senior) Data Scientist (Analytics) to join Wolt’s Courier & Logistics mission!
As the international side of the Doordash business, Wolt is active in 27 countries outside of the US and growing. Together with functional and cross-functional partners, you’ll tackle highly impactful challenges to craft the most-loved platform for gig workers, and empower local businesses with our robust logistics system. Ultimately, your efforts will connect consumers to everything they desire.

Responsibilities

As a Data Scientist at Wolt, you’ll dive deep into our data to tackle crucial business challenges. You will diagnose problem areas, develop new metrics, design experiments, uncover foundational causal relationships in our data, and influence strategic decisions. This role combines scientific expertise with stakeholder engagement, positioning you at the forefront of driving business outcomes. You will work closely with Product, Engineering, Design, and Operations teams to guide measurement strategies and tactical decisions, helping to expand the Wolt platform globally.
In this role you will use data to optimize and quantify the onboarding of couriers and help us to reach the right number of couriers in the most efficient way to maximize the business outcome.

From day one you will work on questions such as

  • What’s the business impact of one missing courier or a delay in the onboarding process?
  • How do referrals impact courier performance and retention?
  • What are the main blockers in the onboarding funnel and how can we improve them?
  • Do automated processes perform better or worse than manual ones and how much so?

You will be the expert from the data side to answer the questions and own exploration, tracking, and experimentation.

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