Senior Data Scientist, Catawiki

• You are an all-around data scientist with 5+ years of experience who is as comfortable diving into the internals of a model implementation as discussing new opportunities for ML with product stakeholders
• Can use your broad skill set to solve challenging ML problems and have a high throughput
• Are familiar with a range of different ML techniques and use that to select a good solution for their projects
• Are able to considers the operating/deployment constraints of large models and implement solutions accordingly
• Give guidance and unblock others in your team by supporting them with advice and new techniques
• Have a strong quantitative background (statistics, ML, sciences, etc.)
• Are fluent in Python and capable with SQL
• Have deployed ML models serving real time responses on a large scale
• Have demonstrable experience in building robust data products that created value for the company you worked for
• If you’re excited about this role but don’t meet every requirement, we still encourage you to apply anyway. You may be just the right candidate for this or other roles

Desirable

• As a Senior Machine Learning Data Scientist in the Data Team at Catawiki you will focus on delivering scalable and impactful data products. You’ll work closely with our product and development teams while implementing your work. This includes:
• Building production-ready models that apply machine learning and advanced data science techniques within our product offering by writing robust and scalable code
• Finding data science opportunities by exploring our extensive mobile and web data, and applying machine learning and advanced statistics to tackle these real-world business problems
• Being pragmatic when needed but also able to go deep into a problem, applying your expert knowledge of machine learning techniques
• Working with cross-disciplinary teams involving product owners, developers, UX designers, and business analysts in identifying improvements and new use cases for ML
• Deploy scalable models using Kubernetes on cloud infrastructure, producing real-time, asynchronous, and batch predictions