Applied Scientist - Assortment Recommendation | Applied Scientist - Assortment Recommendation (all genders)
Zalando SE
Job Summary
This role is for an Applied Scientist focused on optimizing assortment recommendations to enhance the customer shopping experience and improve stock management for a leading e-commerce platform. The successful candidate will join a team of applied scientists and engineers, contributing to the development of next-generation forecast models that inform crucial stock planning decisions. Day-to-day responsibilities include active participation in roadmap definition, collaborating with product managers and engineers, and building expertise in uncertainty measures for ML models. The position requires end-to-end technical ownership, from prototyping production-friendly solutions to managing training components in production, utilizing common programming tools and data science libraries. This is an exciting opportunity for a professional with a strong quantitative background and practical experience in developing impactful machine learning data products.
Required Skills
Education
Master's degree or above (PhD) in quantitative economics, statistics, mathematics, computer science, operation research, or a related quantitative discipline with a strong theoretical foundation in statistics, Bayesian inference, probability theory, and/or deep learning.
Experience
- At least 2 years of industry (non-academic) experience in developing ML data products
- 1 year of industry experience with a PhD degree in developing ML data products
- Extensive experience with Python and its associated data science libraries
- Proficient in programming with exposure to ML pipeline design patterns
Languages
Additional
- Not specified
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