Tech Lead - TikTok Ecommerce Recommendation Algo

TikTok
San Jose, California

About the job

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

• Lead the team to design, build, optimize large-scale (10 million to 100 million) e-commerce recommendation algorithm pipeline

• 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.

• Build, expand and manage the global e-commerce recommendation team from scratch

• Collaborate with strategy team, product managers, policy team and ops team to define products and drive initiatives from engineering viewpoints

Qualifications

Minimum

• Bachelor's degree in Computer Science or related technical field

• 3+ years of working experience in one of the following fields: recommendation system, online advertising, information retrieval, natural language processing, machine learning, large-scale data mining, or related fields.

• Experience in leading an engineering team

• Strong sense of responsibility and good at communication and teamwork

• Passionate about solving complex and challenging problems

Preferred

No preferred qualifications listed.