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Senior Applied Scientist, Operations Research
WoltBerlin, Germany; Helsinki, Finland; Stockholm, SwedenPosted 19 May 2026
Job Description
About Wolt
At Wolt, we create technology that brings joy, simplicity and earnings to the neighborhoods of the world. In 2014 we started with delivery of restaurant food. Now we’re building the delivery of (almost) everything and you’ll find us in over 500 cities in 30 countries around the world. In 2022 we joined forces with DoorDash and together we keep on dreaming big and expanding across the globe. Working at Wolt isn’t always easy, but it’s definitely exciting. Here you’ll learn more, build more, and ship more than in most other companies. You’ll be challenged a lot, but also have a lot of fun on the way. So, if you’re a self-starter with drive and entrepreneurial spirit, this could be the ride of your life.
Our Applied Scientists at Wolt build and deploy Applied Science and Machine Learning solutions to address a wide variety of challenging business problems. Utilizing a spectrum of methodologies including statistical analysis, machine learning, deep learning, and operations research, they improve critical processes within Wolt's online delivery platform and business operations. Their contributions significantly impact all 31 countries in which we operate.
The work consists of owning applied science use cases as part of a product development team; starting from identifying opportunities, to developing and prototyping a solution, all the way to supporting its deployment, monitoring, and improving it in production. We use a variety of technologies and tools including Python, SQL, Snowflake, Flyte, MLflow and Seldon Core to get the job done, and are constantly looking for ways to improve how our applied scientists work.
We are looking for an Applied Scientist specialized in Operations Research to be embedded into our cross-functional Logistics Assignments team. Together with other operations researchers, software engineers, and product, design and analytics people, you would develop algorithms and solutions for efficiently assigning tasks to our Courier Partners. Our task assignments engine is at the core of our logistics system. This is a unique opportunity to work on challenging operations research problems at a large scale. You will have great potential to make a significant impact by tackling complex problems and building new solutions to improve the efficiency of our logistics operations and, hence, our business.
📍This role can be based in one of our tech hubs in Helsinki, Berlin, or Stockholm .
Our humble expectations
Solid experience in Operations Research and production-level Applied Science projects, with the ability and interest to own solutions end-to-end: from prototyping to deployment, maintenance, and continuous improvement.
Deep understanding of Operations Research principles and a strong foundation in statistics .
Proven ability to work in highly complex domains, break down challenging problems into manageable parts, and develop innovative solutions. Comfortable explaining technical concepts to non-technical stakeholders.
Experience within logistics , optimization, or operational research domains is highly valued.
Knowledge of experimental design and analysis , including A/B testing .
Strong engineering skills with fluent coding ability in Python (our primary language for Applied Science projects) and experience working with databases ( SQL). Experience with C++ or Go is a plus.
You value good software engineering practices, understand MLOps, and take pride in the quality of your code and the performance of your solutions.
Ideally, a degree in a field related to Operations Research or Applied Science .
Analytical, curious, business-oriented, and proactive mindset, with the ability to independently drive work forward while collaborating effectively with stakeholders across teams.
Next steps
The position will be filled as soon as we find the right person, so make sure to apply as soon as you realize you really, really want to join us!
The compensation will be a negotia ... (truncated, view full listing at source)
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