SCIENCE AT DELIVEROO
We’re the EMEA Science team.
We use data, experimentation and scientific thinking to solve the complex challenges behind local commerce. Working across product and business teams, our Data Scientists and Analytics Engineers shape decisions that improve experiences for millions of consumers, merchants and delivery partners.
Part of DoorDash, our EMEA Science team brings together Data Scientists and Analytics Engineers from across Deliveroo and Wolt.
Our mission is to drive business growth through scientific, data-driven insights. Some work in central teams, while others are embedded directly within product and business squads. Together, they use data to refine algorithms, uncover opportunities and guide decisions that improve experiences for consumers, merchants and delivery partners across both brands.
Roles we hire for
Analytics Engineer
What you’ll work on
Build and maintain clean, tested and well-modelled datasets, SQL transformations and data products using tools such as Snowflake and Looker.
Who you’ll work with
Partner with Data Scientists and Product Engineers to ensure source data meets downstream needs and analytical models are robust, intuitive and easy to use.
The impact you’ll have
Make trusted data easier to discover and use, enabling teams across the business to answer questions and make better decisions.
Data Scientist
What you’ll work on
Own analytical problems end to end, turning broad, ambiguous questions into structured approaches using experimentation, causal inference and analysis.
Who you’ll work with
Work within Product, Operations and Platform teams, partnering with technical and non-technical stakeholders across the business.
The impact you’ll have
Turn evidence into clear recommendations that influence roadmaps, improve our three-sided marketplace and raise the quality of decision-making.
Latest Roles
Watch our team in action
Igor Khomyanin: Holdout Experiments; Patterns, Trade offs, and Lessons from Real Projects
Ella Johnson-Watts: High quality experimentation at Deliveroo; The vision, implementation and reflection
Mahana Mansfield: The Qualities That Great Data Scientists Share – and How to Assess Them
Building a world-class data org | Jessica Lachs (VP of Analytics and Data Science at DoorDash)
Explore our thinking
- Data Science
Experimentation Principles at Deliveroo
- Leadership
Starting an Analytics Org From Scratch
- Analytics Engineering
Analytics Engineering at Wolt
- Smarter targeting
How DoorDash’s brand affinity powers smarter targeting
Hear from our leaders
My own path into data science was unconventional. I came to the UK to pursue a PhD in Maths while training as a professional judo athlete, then moved into software engineering before finding my way into data science. That experience has shaped how I build teams: great Data Scientists and Analytics Engineers can come from many different backgrounds and paths.
What makes the work here particularly interesting is the complexity of the marketplace. A seemingly simple change can have effects across consumers, merchants, riders, our economics, and the broader system. That creates fascinating problems to solve. You need to frame the right question, understand how the system fits together, experiment rigorously, and turn what you learn into action.
I care a lot about impact and about being pragmatic in how we get there. If you enjoy solving hard problems, learning quickly, and seeing your work shape real products and decisions across markets, you’ll find a lot to get excited about here!
My own path into data science was unconventional. I came to the UK to pursue a PhD in Maths while training as a professional judo athlete, then moved into software engineering before finding my way into data science. That experience has shaped how I build teams: great Data Scientists and Analytics Engineers can come from many different backgrounds and paths.
What makes the work here particularly interesting is the complexity of the marketplace. A seemingly simple change can have effects across consumers, merchants, riders, our economics, and the broader system. That creates fascinating problems to solve. You need to frame the right question, understand how the system fits together, experiment rigorously, and turn what you learn into action.
I care a lot about impact and about being pragmatic in how we get there. If you enjoy solving hard problems, learning quickly, and seeing your work shape real products and decisions across markets, you’ll find a lot to get excited about here!
