Data & Analytics

Data science at Wolt

Data Scientists at Wolt are working with machine learning for the most important processes and parts of Wolt’s online delivery platform and business operations that affect all the 30 countries we operate in.

Embedded in cross-functional business and product teams, we help teams to solve business problems with anything from forecasting to predicting and optimizing processes. Data scientists have an end-to-end role in the team. They help investigate the business problem, build models, take them to production, and maintain them.

Our data scientists work on things like:

• Logistics Optimization: We improve our logistics platform to make Wolt’s services as efficient as possible – and we do this by being smart about how we allocate tasks to available couriers. We estimate several different time steps during the delivery journey, and use those as inputs into our optimization process.

• Delivery ETA: Our models produce around 57 billion delivery time estimates daily, and those are used in the different pages of our app to heavily influence the overall customer experience.

• Personalization: We provide personalization and recommendations at multiple places along the customer journey. Making recommendations is complex — changes in the customer’s location also changes the selection of venues available to them, while user intent can vary a lot. We generate recommendations for over seven million users daily.

• Marketing: We’re building up our marketing data platform that helps us automate campaigns and optimize spend across channels while understanding the impact of our marketing activities.

Data Analytics team

Our data analysts are embedded in our business and product teams to be close to the decisions we’re making for the Wolt platform. We play a key role in helping teams understand the behavior of our customers and partners.

The role of our data analysts is to help our teams focus on things that have the biggest impact – always ensuring we make data-informed decisions relating to our product. We advocate experimentation and measure incremental gains of the changes done to the platform. In short, we translate numbers into actionable insights for our teams.

Our Data Analysts work on things like:

• Experimentation of new ideas — we use our internal A/B testing platform to validate and measure the impact of any changes we make. Understanding that is key to how we build Wolt.

• Research projects to deep-dive into user data patterns. For example, investigating the main drivers of customer churn to help us better retain customers.

• Impact analysis to quantify the impact of potential changes. For instance, understanding the effect on purchase volume if the delivery radius of a city is reduced for some reason. This helps us shed light on the most impactful problems to fix, and also which opportunities to go for.

Business Intelligence team

Wolt invests heavily in self-service analytics. We enable our colleagues at Wolt to make smarter, data-informed decisions by providing data and the tools to access and analyze it. And with a global team of thousands of employees, we do this at scale. 

We practice the craft of business intelligence by having high standards for data quality, transforming raw data into information, and exposing the information in the most user-friendly and well-documented format possible. We also provide self-study materials and training sessions for the business.

Naturally, self-service analytics only works if the data is modeled in a user-friendly way while maintaining the data warehouse and metadata with great care. This is something our team excels in.

Our Business Intelligence developers work on things like:

• Making sure everyone working with analytics has a great user experience — performant data and BI platforms form the foundation of analytics

• Data integrations, derived datasets, data pipelines, data models, metrics, and dashboards

• Looking into how we can empower our teams to use data

• Training our teams to work with data in an effective, independent manner

• Providing user-friendly documentation and tutorials for everyone working with data at Wolt

• Creating best practices on how to develop data and utilize it in daily work

• Providing hands-on help and support to our users on how to use and interpret our data