Data Scientist, Wallapop

• Problem-Solving Core: A degree in a quantitative field (Engineering, Math, Stats, Physics) or equivalent practical experience in data science
• Software Mindset: Proficiency in Python and the ability to write clean, maintainable code for ML applications
• Production Experience: A proven track record of building and deploying data products that solve business challenges in a production environment
• Data Fluency: Advanced SQL skills and experience handling large datasets (knowledge of libraries like Pandas or Scikit-learn is highly valued)
• Growth Orientation: A desire to learn new technologies and an ability to adapt your technical approach to the problem at hand
• Algorithmic Foundation: Solid understanding of Machine Learning fundamentals, specifically as they apply to personalization or recommender systems (e.g., collaborative filtering, content-based filtering, or deep learning for embeddings)

Desirable

• You will own the whole product lifecycle, from gathering the data needed to prototyping, building, evaluating, and iterating end-to-end solutions, all within Wallapop’s Data Platforms
• Additionally, you will have the opportunity to work on internal research projects, exploring new technologies and building PoCs that could ultimately become data products or insights, and be valuable for Wallapop
• Collect, clean, and analyze large datasets from diverse sources, including user behavior, transactions, and market trends
• Develop and implement machine learning models to solve business problems, such as personalized recommendations, fraud detection, and pricing optimization
• Work with multidisciplinary Product teams to identify opportunities and gather requirements (both in terms of product and tech)
• Own initiatives from start to deployment: ideation, data processing (ETL), modeling, testing, deployment, monitoring and iteration (w/ help from Engineering partners)
• Proactively suggest, research and prototype new ideas for data and ML solutions
• Communicate complex data insights to both technical and non-technical audiences, including stakeholders involved in marketplace operations

• Please, note that all interviews take place remotely over hangouts
• Intro Call - run by talent acquisition, focus on providing more information about the role and the company as well as going over your experience, motivation, and expectations. This usually takes 45-60 minutes
• Task - you will be assigned a task to submit within 5 up to 7 days where you will be able to showcase your expertise and technical skills required for the role
• Expertise Interview - run by the hiring team, focus on the required core skills and the ability to deliver in a given context. This usually takes 60-90 minutes
• Stakeholder Interview - run by relevant stakeholders reflecting the reality of the context of the role, focus on the ability to collaborate & deliver in a cross-functional set-up. This usually takes 60 minutes
• Culture Interview - run by culture interviewers, focus on adherence to Wallapop's purpose and business proposition. This usually takes 60 minutes
• Offer - should you be the right candidate, your offer will be discussed over a call with talent acquisition and will then be confirmed in writing