Machine Learning Engineer/ Applied Data Scientist, E-Commerce Risk Control - USDS
TikTok.com
Office
San Jose, California, United States
Full Time
About the team
The E-Commerce Risk Control team works to minimize the damage of inauthentic behaviors on Tiktok E-Commerce platforms, covering multiple classical and novel business risk areas such as account integrity, incentive abuse, malicious behaviors, brushing, click-farm, information leakage, etc.
In this team you'll have a unique opportunity to have first-hand exposure to the strategy of the company in key security initiatives, especially in building scalable and robust, intelligent and privacy-safe, secure and product-friendly systems and solutions. Our challenges are not some regular day-to-day technical puzzles -- You'll be part of a team that's developing novel solutions to first-seen challenges of a non-stop evolution of a phenomenal product eco-system. The work needs to be fast, transferable, while still down to the ground to make quick and solid differences.
Responsibilities:
- Invent, implement, and deploy cutting-edge machine learning algorithms, build prototypes, and explore entirely new conceptual solutions to respond to and mitigate business risks in TikTok e-commerce/platforms, including but not limited to fraudulent merchants, cheating influencers, malicious users, information security issues, and cross-domain risk challenges.
- Leverage techniques such as representation learning, graph models, deep learning, transfer learning, and multi-task learning to improve the efficiency of problem detection, thereby rapidly blocking risks and optimizing various metrics in the e-commerce community ecosystem.
- Monitor and attribute key metrics to quickly detect changes in risk and business trends, proactively identify potential attacks, continuously refine and adjust risk control strategies, and drive risk governance development and business model improvements.
- Mine and analyze massive e-commerce content and user behavior data to build both short-term and long-term user profiles, improve model precision and recall, and enhance robustness, automation, and generalization capabilities.
- Advance risk ML capabilities in privacy/compliance, interpretability, risk perception, and analysis; innovate models and algorithms tailored to the characteristics of content e-commerce, and build an industry-leading content e-commerce risk control algorithm system.
In order to enhance collaboration and cross-functional partnerships, among other things, at this time, our organization follows a hybrid work schedule that requires employees to work in the office 3 days a week, or as directed by their manager/department. We regularly review our hybrid work model, and the specific requirements may change at any time.
The E-Commerce Risk Control team works to minimize the damage of inauthentic behaviors on Tiktok E-Commerce platforms, covering multiple classical and novel business risk areas such as account integrity, incentive abuse, malicious behaviors, brushing, click-farm, information leakage, etc.
In this team you'll have a unique opportunity to have first-hand exposure to the strategy of the company in key security initiatives, especially in building scalable and robust, intelligent and privacy-safe, secure and product-friendly systems and solutions. Our challenges are not some regular day-to-day technical puzzles -- You'll be part of a team that's developing novel solutions to first-seen challenges of a non-stop evolution of a phenomenal product eco-system. The work needs to be fast, transferable, while still down to the ground to make quick and solid differences.
Responsibilities:
- Invent, implement, and deploy cutting-edge machine learning algorithms, build prototypes, and explore entirely new conceptual solutions to respond to and mitigate business risks in TikTok e-commerce/platforms, including but not limited to fraudulent merchants, cheating influencers, malicious users, information security issues, and cross-domain risk challenges.
- Leverage techniques such as representation learning, graph models, deep learning, transfer learning, and multi-task learning to improve the efficiency of problem detection, thereby rapidly blocking risks and optimizing various metrics in the e-commerce community ecosystem.
- Monitor and attribute key metrics to quickly detect changes in risk and business trends, proactively identify potential attacks, continuously refine and adjust risk control strategies, and drive risk governance development and business model improvements.
- Mine and analyze massive e-commerce content and user behavior data to build both short-term and long-term user profiles, improve model precision and recall, and enhance robustness, automation, and generalization capabilities.
- Advance risk ML capabilities in privacy/compliance, interpretability, risk perception, and analysis; innovate models and algorithms tailored to the characteristics of content e-commerce, and build an industry-leading content e-commerce risk control algorithm system.
In order to enhance collaboration and cross-functional partnerships, among other things, at this time, our organization follows a hybrid work schedule that requires employees to work in the office 3 days a week, or as directed by their manager/department. We regularly review our hybrid work model, and the specific requirements may change at any time.
Machine Learning Engineer/ Applied Data Scientist, E-Commerce Risk Control - USDS
Office
San Jose, California, United States
Full Time
September 4, 2025