The data backbone behind ML pricing.
ZalandoBuilt the data infrastructure behind machine-learning demand forecasting and price optimization across 500K+ products.
The challenge
Zalando's demand prediction and price optimization ran on heuristics that needed constant manual tuning. The data science teams wanted to move to modern machine learning, but they lacked a scalable, reliable data foundation to build on.
What I did
I owned the data engineering side: designing, building, and testing the data infrastructure that several data science teams depended on. That meant dependable pipelines, efficient processing of large-scale transactional and behavioral data across 500K+ products, and a foundation that ML models could be trained and deployed on without friction.
Outcomes
- Gave ML teams a high-performance foundation for model development and deployment.
- Helped replace manual heuristics with machine-learning-driven pricing.
- Supported Zalando's shift toward dynamic, AI-driven pricing at scale.
- Reduced repetitive manual work for the wholesale teams.
Ilya is an exceptional engineer and I was happy to work with him at Zalando. He is a very supportive team player and greatly helped maintaining PostgreSQL based ETL processes as well as the communication with the Business Intelligence and Analytics departments. His previous experiences with databases, data processing and fundamental computer science research helped to largely improve the quality of Data Science at Zalando.
Valentine GogichashviliRenowned data leader, built Zalando's first database team, laying the foundation for a data infrastructure that now serves millions of customers.
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