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 28 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 deploying, maintaining, 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 to be embedded into our cross-functional Fulfillment team in Logistics.  Together with other applied scientists, software engineers, and product, design and analytics people, you would develop algorithms and solutions for predicting accurate task fulfillment estimates. This fulfillment estimates fuel our logistics engine at the core of our business; accurate estimates enable efficient task allocation to courier partners, minimizing waiting time. You would work on challenging machine learning problems, at a high scale with strong heterogeneity in our data. You will have great potential to make a significant impact by tackling complex problems and building new solutions to improve our operations.

📍This role can be based in one of our tech hubs in Helsinki, Berlin, or Stockholm. 

• You have plenty of hands-on experience with production-level Applied Science and Machine Learning projects — from prototyping to deploying, maintaining and improving production deployed solutions (i.e. ability and interest to handle a use case end-to-end);

• You have a deep understanding and experience with machine learning and a solid understanding of statistics. You understand what it takes to train models on heterogenous data, and build inference services for a real-time environment;

• Proven ability to tackle highly complex domains, break them down into solvable problems and develop novel solutions to them. You can fluently discuss technical concepts and matters with non-technical stakeholders;

• Preferable experience with logistics domains, great if you have worked on projects within optimization and operational research fields;

• Knowledge of experimental design and analysis, such as A/B testing;

• You have solid engineering skills and code fluently in Python, which is our main language for applied science projects, and experience with databases (SQL). You value good software engineering practices, understand MLOps, and take pride in the quality of your code and the performance of your solutions;

• Ideally, you’ll also have a degree in a field relevant to Applied Science;

• You’re an analytical, curious and proactive problem solver. We’re looking for a self-starter with a drive to get things done. Also as well a good communicator, willing to collaborate with people inside and outside of the team.