Applied Scientist- E-Commerce Recommendation

Position: Applied Scientist- TikTok E-Commerce Recommendation
E-commerce is a new and fast growing business that aims at connecting all customers to excellent sellers and quality products on Tik Tok Shop, through E-commerce live-streaming, E-commerce short videos, and commodity recommendation. We are a group of applied machine learning engineers and data scientists that focus on E-commerce recommendations. We are developing innovative algorithms and techniques to improve user engagement and satisfaction, converting creative ideas into business-impacting solutions.

We are interested and excited in applying large scale machine learning to solve various real-world problems in E-commerce.

Responsibilities:

* Participate in building large-scale (10 million to 100 million) e-commerce recommendation algorithms and systems, including commodity recommendations, live stream recommendations, short video recommendations etc in Tik Tok.

* Build long and short term user interest models, analyze and extract relevant information from large amounts of various data and design algorithms to explore users' latent interests efficiently.

* Design, develop, evaluate and iterate on predictive models for candidate generation and ranking(eg. Click Through Rate and Conversion Rate prediction) , including, but not limited to building real-time data pipelines, feature engineering, model optimization and innovation.

* Design and build supporting/debugging tools as needed.