ISyE Seminar Series: Yoni Acriche

Yoni Acriche

"Finding the perfect salesperson: Advanced job matching models in Bravado"

Presentation by Yoni Acriche
Co-Founder and Chief Data Scientist
Bravado, Austin

3:30 pm - Seminar
4:30 pm - Reception, coffee and cookies

About the seminar:

The performance of the sales team directly affects companies’ revenue, and the alternative cost of a bad hire is often much greater than the hire’s annual salary. However, only 43% of sales hires hit their revenue targets in 2021, and the average tenure of a salesperson decreased to less than 12 months. Given this, it’s no surprise that Sales is considered one of the most challenging roles to hire. Bravado, a Series B startup, aims to ease the process and minimize the risks associated with hiring sales employees. By building the world’s largest professional network for salespeople, Bravado uses a novel data-driven approach to make hiring easier, increase transparency, and help candidates and companies find better fits. In this presentation, I will cover the data architecture of Bravado’s sales hiring matching solution, discuss the strategy of picking the right Machine Learning approach, and explain in detail the neural network model that powers the matching algorithm and its empirical results.

Bio:

Yoni Acriche is the Co-Founder and Chief Data Scientist of Bravado, a series B startup funded by Tiger Global, Redpoint, and Freestyle VC. In Bravado, Yoni focuses on leveraging Machine Learning to make the community members more successful, and increase the company’s operational efficiency. Prior, Yoni was the Head of Data Science at Salespredict (acquired by eBay), and the Head of Seller Experience Research at eBay. His research focuses on using large-scale neural network language models to increase the efficiency of online marketplaces. His work was published in top Machine Learning conferences such as ACL, WSDM, and ACM, as well as in the Harvard Business Review.

Papers:

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Product Titles-to-Attributes As a Text-to-Text Task

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Start date
Wednesday, Nov. 30, 2022, 3:30 p.m.
End date
Wednesday, Nov. 30, 2022, 4:30 p.m.
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