Each day at Zulily we add 9,000 products to our online store and process more than 5 billion clicks from online shoppers. That is more virtual inventory than you'll find in the warehouses of many retailers, and it's by design. We've built a supply chain where we hold only some goods: most of the time, we don't purchase inventory until our customers have, so we are able to pass down savings from our unique supply chain down to our customers around the world. To the customer, that means a constantly changing and new shopping experience. Our system works only if we can ensure that both shoppers and suppliers move quickly.
In a world where many retailers struggle to keep up with tech-driven customer demands, such as mobile sales and personalization, online retailer zulily has made it part of its DNA. Gaurav Tandon, zulily's director of data science and machine learning, notes that embracing tech at its core allows the retailer, which saw net sales of 1.6 billion in 2017, to be "different than commoditized search and transactional ecommerce." Zulily seeks to create an entertaining and engaging "browse and discover" shopping experience online through both its website and app, and although the customer sees a seamless experience, it takes a lot of technical complexity, says Tandon. In fact, the company launches 9,000 products through more than 100 sales events a day. Because the company launches a new assortment of products daily across a variety of product categories, every day is different and, according to Tandon, requires flexibility in all parts of the business.
While its ubiquitous appearance in marketing copy may have diluted its meaning somewhat, true artificial intelligence is already powering the products, services and customer experiences of some of the tech industry's biggest players. At its heart, artificial intelligence is perfect for several clearly defined use cases. Among them: UX/UI personalization, automated data management and analysis of that data at scale. These applications make AI particularly important to e-commerce and big data companies, along with insurance and fintech companies that can build predictive models on technology that analyzes huge datasets. These engineering leaders are at the forefront of AI's evolution in their respective fields, and they spoke to Built In about the trends they're seeing across the industry.
Microsoft did what it does best: waited to see signs of success (four years, in this case) then copied the offering and later integrated it into its other products. A third approach is to copy a niche product. Allbirds acquired a cult following by developing a line of wool shoes sourced in an environmentally responsible manner. In response, Amazon copied the top-selling product almost point-for-point and sold it online for nearly half the price. Despite this predatory behavior -- and the resulting reluctance of some venture capitalists to invest -- a few startups have managed to survive beyond their early stages and become sizable players in the same space as the tech giants. On the surface, it looks as if they succeeded due to luck or lack of interest on Big Tech's part. In reality, though, these challengers succeeded by using the companies' strengths against them. This strategic move, although counterintuitive at first, can lead to copy-proof innovation.
Unique products from up-and-coming brands are featured alongside favorites from top brands, including clothing, home decor, accessories, toys and gifts, giving customers something new to discover each morning at an incredible price. We are fast-paced, innovative, data- and metric-driven. As a Machine Learning Engineer, you build and scale our machine learning and experimentation platforms and incorporate new features to drive model predictions. This is your opportunity to make a big impact – ranging from delivering a personalized customer experience to optimizing advertising spend to improving merchandising efficiencies. You work on cross-functional initiatives, such as service and container platforms, machine learning platforms, data security, distributed memory services, service monitoring, real-time experimentation, data transformations, and much more.