Senior Data Scientist, Wallapop

• Bachelor or MsC degree in engineering, mathematics, statistics, physics, or another quantitative field
• Strong coding skills (Python is a must, but other languages are also valued), capable of building robust data and ML software
• Experience building complex data products and ML solutions to real-life problems in a production-oriented environment, identifying the right techniques to solve the problem at hand
• Experience with common libraries and frameworks such as Pandas, Numpy, Scikit-learn, TensorFlow, Keras, PyTorch
• Advanced in SQL, capable of getting the data required from the appropriate sources, and building and maintaining data pipelines if needed (experience in tools such as Airflow or DBT would be a plus)
• Excellent communication and presentation skills, with the ability to explain data insights to business stakeholders in the context of marketplace dynamics

Desirable

• As a Data Scientist in the Wallapop you will be working on the Data Science initiatives aimed at improving the whole marketplace dynamics
• You will work closely with other Data Scientists, Product Managers, Engineers, UX Researchers, and Analysts to understand the product requirements and explore possible data and ML solutions
• 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