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About the Role

We are looking for an Applied Scientist to advance the machine learning models behind our recommendation systems. You’ll work on challenging applied ML problems where model quality, product decisions and customer experience are tightly connected. This is an opportunity to take ideas from problem framing and data analysis through experimentation, production deployment and measurable customer impact.

Responsibilities

• Design, develop and improve recommendation, ranking and retrieval models that surface relevant restaurants, dishes, items and content to customers.
• Own applied ML problems end to end: frame the problem, analyze data, develop models, define offline evaluation, run experiments and monitor production performance.
• Develop methods that balance relevance with product and customer needs, such as diversity, availability, business constraints and changing user intent.
• Collaborate closely with Software Engineers, ML Engineers, Product Managers and Analysts to turn scientific insights into reliable customer-facing products.
• Evaluate and apply state-of-the-art ML methods where they meaningfully improve recommendation quality, robustness or efficiency.
• Contribute to a high bar for applied-science practice through technical reviews, knowledge sharing and thoughtful experimentation.

Requirements

• Substantial hands-on experience applying machine learning to real-world problems and a track record of bringing models from development into production; a PhD with relevant applied research experience is equally welcome.
• Experience with recommendation systems, ranking, retrieval, personalisation, or closely related ML problems.
• Ability to independently turn an ambiguous customer or product problem into a well-scoped ML approach, make sound trade-offs and drive it to a measurable outcome.
• Proficiency in Python and experienced with modern ML frameworks and large-scale data processing.
• Understanding of how to evaluate ML systems rigorously, including offline metrics, experiment design and interpreting online results.
• Ability to communicate complex technical ideas clearly and work effectively with cross-functional partners.

Skills

• Machine learning
• Recommendation systems
• Ranking
• Retrieval
• Personalisation
• Python
• ML frameworks
• Large-scale data processing
• Offline metrics
• Experiment design
• Online results interpretation

Education Level

• PhD

Benefits

• Opportunity to work on recommendation problems with direct, measurable impact on how customers discover relevant content.
• Collaboration with experienced scientists and engineers across DoorDash, Deliveroo and Wolt.
• Opportunity to shape the next generation of the customer experience.
• Opportunity to create a personalized development plan that builds on strengths and develops new capabilities.

About the Company

• Wolt creates technology that brings joy, simplicity and earnings to the neighborhoods of the world.
• Wolt started with delivery of restaurant food and now builds the delivery of (almost) everything.
• Wolt is present in over 500 cities in 30 countries.
• Wolt joined forces with DoorDash in 2022.
• Wolt is part of DoorDash - together they form one of the world’s largest local commerce platforms.
• Wolt builds recommendation systems that help customers discover the most relevant restaurants, dishes and items throughout their Wolt experience.

Equal Opportunity

• Wolt is committed to growing and empowering a more inclusive community within the company, industry, and cities.
• Wolt hires and cultivates diverse teams of people from all backgrounds, experiences, and perspectives.
• Wolt believes that true innovation happens when everyone has room at the table and the tools, resources, and opportunity to excel.