Applied Scientist- TikTok E-Commerce Recommendation

Description

E-commerce is a new and fast growing business that aims at connecting all customers to excellent sellers and quality products on TikTok 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 TikTok.
• 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.

Requirements

Minimum Qualifications:
• MS/PhD in Computer Science, related technical field or equivalent industrial research experience.
• 3+ years of working experience in one of the following fields: recommendation algorithm, online advertising, information retrieval, natural language processing, machine learning, large-scale data mining, or related fields.
Preferred Qualifications:
• Familiar with at least one of the mainstream deep learning frameworks, like TensorFlow or PyTorch.
• Passionate about solving complex and challenging problems