Applied Scientist - Merchandise Integrity | Applied Scientist - Merchandise Integrity (all genders)

Job Summary

This Applied Scientist role focuses on building and maintaining cutting-edge anomaly detection systems to ensure merchandise integrity and optimize customer experience at Zalando. The core responsibility involves owning the entire algorithm development lifecycle, from identifying opportunities and conceptualizing solutions to productionizing state-of-the-art risk scoring models. Day-to-day tasks include collaborating with product managers and engineers, communicating complex technical results to diverse stakeholders, and continuously improving model pipelines using common programming tools. Candidates must possess a Master's degree or higher in a quantitative discipline, coupled with a strong theoretical foundation in machine learning methodologies. A minimum of three years of industry experience in developing anomaly detection and risk scoring systems is required, along with extensive proficiency in Python for writing production-quality code. This position is ideal for a self-starter who enjoys solving complex problems and driving impactful solutions in a fast-paced e-commerce environment.