Stylight – a centerpiece for the Fashion Data Industry


At Stylight, we have a priceless asset: more than 100 million datasets of fashion and apparel products over a timeline of ten years. These records have been enriched with attributes from machine learning and external sources. More than 700 million images and a 1 billion attributes – continuously quality assured – complete our data lake. And this is only one side of the medal.

On the other hand side, we serve over 12 million search requests each month. Out of these search queries we can derive trends in the industry, improve user perception of products as well as learn how pricing and deals affects the attractive of certain fashion products.

Working as a centerpiece at the crossing of these two data streams is super exciting. Hence at Stylight almost every engineer is somehow involved in

  • Product classification
  • Item tagging and attributing
  • Intelligent Pricing and Deals
  • Product Color detection and matching
  • Ranking and Trending
  • Traffic predictions and recommendation

in order to fuel the Stylight Search Engine. No wonder, data engineering and machine learning are key skills in modern software engineering.

Ten years go, Stylight started with the promise to make the online fashion shopping experience more enjoyable. This business model has proven to work out and makes customers happy on a daily basis. In this timeframe the underlying basis of our fashion data search engine has radically changed.

Coming from highly specialized components, the architecture became more flexible to cover a variety of use cases for different industry verticals. The Stylight data pipeline has elaborated to a data provider for future use cases as we aggregate the data around each product.

The ability to aggregate, process and learn from data is one of the key challenges in the fashion and apparel industry these days. And this is where Stylight can act as an enabler for our partners with new services, additional functionality and advanced reporting and trending — fully data driven.

If this is an environment that inspires to work and contribute, have look at our job offerings in engineering as well as other domains.


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