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# Data Scientist, Payment Risk - USDS

- **Company:** Tiktok Inc.
- **Location:** San Jose, CA, United States
- **Experience:** Experienced
- **Salary:** $144,000.0 - $329,334.0
- **Contract:** Permanent contract
- **Skills:** Microsoft Excel, Artificial Intelligence, Data Analysis, Computer Engineering, Data Security, Payment Systems, R (Programming Language), Apache Hive, Python (Programming Language), Machine Learning, Payment Gateway, SQL Databases, Apache Spark, Tools for Reporting
- **Published:** June 5, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=c4f08302f5a1c66e

## About the Role

Do you have experience in Team leadership?, Minimum Qualifications: - Master's degree (Ph.D. is a plus) in Statistics, Mathematics, Finance, Computer Science Engineering or a similar Quantitative field, or equivalent practical experience - 3 years of experience in the payment risk industry, with experience in risk management practices, fraud analytics - Strong analytical skillset and experience with analytics tools (Excel, SQL, R, Python, Hive, Spark, and other statistical or data analysis packages) - Results-oriented and comfortable working in a fast-paced environment requiring prioritization and ownership Creative problem solving and critical thinking skills - Excellent written and verbal communication skills and ability to influence others, with strong leadership skills and ability to manage Preferred Qualifications: -Strong experience applying AI or Machine Learning techniques to solve complex, large-scale risk problems are preferred

## Description

Team Intro: The Payment team of U.S. Data Security department of TikTok provides payment solutions - including payment acquisitions, disbursements, transaction monitoring, payment method management, foreign exchange conversion, accounting, reconciliations, and so on - to ensure that users have a smooth and secure payment experience on TikTok platform. Responsibilities: - Establish a holistic risk management framework for mitigating the risk of payment fraud, credit losses and financial abuse across consumers, merchants, and affiliates - Develop metrics and dashboards to monitor risk, perform scenario planning and simulation testing - Leverage cutting-edge AI and Machine Learning techniques to solve complex risk problems, to improve the efficiency of automated detection and the effectiveness of risk interventions, reducing false positives while maintaining high recall - Deep dive into fraud cases through massive data and ad hoc analysis that identify root causes of risk originations

 - Design risk solutions that introduce appropriate amounts of friction for suspicious users based on suspicious user characteristics and machine learning models - Collaborate with product, engineering and cross function teams to define risk product roadmaps and strategies - Communicate results on a regular basis to stakeholders around the world, including executive leadership


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