# 2022 Intern Projects

DevFeed: [2022 Intern Projects](<https://devfeed.tech/articles/2022-intern-projects-29341.md>)

Original publisher: [Read original article](<https://multithreaded.stitchfix.com/blog/2023/01/03/intern-post/>)

Published: 2023-01-03T09:00:00Z

Content type: article

Language: en

Sources: [Stitch Fix](<https://devfeed.tech/sources/stitch-fix.md>)

Topics: [Algorithms, Complexity](<https://devfeed.tech/topics/algorithms-complexity.md>), [Data Science](<https://devfeed.tech/topics/data-science.md>), [personalization](<https://devfeed.tech/topics/personalization.md>), [Query (disambiguation)](<https://devfeed.tech/topics/query.md>), [Availability](<https://devfeed.tech/topics/availability.md>), [client](<https://devfeed.tech/topics/client.md>)

Tags: [algorithms](<https://devfeed.tech/tags/algorithms.md>), [availability](<https://devfeed.tech/tags/availability.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [personalization](<https://devfeed.tech/tags/personalization.md>), [search](<https://devfeed.tech/tags/search.md>)

## AI overview

This article presents projects by four Stitch Fix interns. One project examined how inventory allocation affects personalized search in Stitch Fix Freestyle, using personalization and relevance metrics and comparing them with client satisfaction while accounting for inventory availability.

## Source excerpt

Earlier this year, we had four fantastic interns join our Algorithms team for 3 months to learn how we harness data science in our work at Stitch Fix. These four interns hail from across the country and spent their time exploring a specific project and advancing their skills in their unique interest areas. In their own words below, they've showcased each of their own projects and the meaningful insights they were able to uncover in their short time with us. Optimizing inventory allocation for client-facing search Maria Olaru, PhD candidate in Computational Neuroscience at University of California - San Francisco In an effort to expand beyond its core business of Fixes, a service where stylists select five personalized items and send them straight to clients' doors, Stitch Fix launched Freestyle. Freestyle is a personalized and instantly shoppable feed of items that clients can directly buy outside of their Fix. Within Freestyle, Stitch Fix recently launched a Search feature, allowing clients to directly query personalized items. I spent my time at Stitch Fix investigating how to optimize Stitch Fix's existing inventory allocation to better serve customers who use Search within Freestyle to search for products. For my project, I chose inventory-related metrics to explore, collected the corresponding data, and compared these metrics to client satisfaction. I chose inventory-related metrics that I could compare to client satisfaction: personalization and relevance. Personalization captures how well our items reflect clients' individual style, while relevance captures accurately our items reflect what a client asked for. With Fix orders, we view personalization as a top metric that predicts client purchases. However, Freestyle differs from Fix in a crucial manner: clients using Fix are not asking for a specific item, whereas clients using Search within Freestyle know what they want, and are explicitly looking for it. Next, I collected data for these metrics. To explore