Data Science Graduate (Global Payment) - 2027 Start - Graduate (Global Payment) - 2027 Start

The Global Payment Tech team is the core engine powering ByteDance's global payment and financial services, spanning payment acquiring, wallets, financial products, insurance, and foreign exchange for users and merchants worldwide.
We span engineering, data, and AI, building high-concurrency, highly available systems across multi-currency, multi-market complexity, and embedding AI into risk control, credit decisioning, and operations automation.
You will grow cross-disciplinary expertise at the intersection of payments, finance, and AI.
The Data Intelligence team within Global Payment is dedicated to driving data-informed decision-making across ByteDance's global payment and financial services businesses. We build core analytics capabilities, metric frameworks, and AI-powered data tools to support products spanning e-Commerce checkout, TikTok Pay, live streaming monetization, and advertising. Our team combines expertise in statistical modeling, causal inference, machine learning, and large-scale data systems to generate insights that directly improve business performance, user experience, and operational efficiency across global markets.
We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume. Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.
Take charge of data analytics for payment and financial segments of global payment business, and conduct in-depth analysis on transactions and financial metrics for TikTok’s core businesses including e-commerce, live streaming and advertising.
- Opportunity Identification: Gain in-depth insights into user behaviors, deliver optimization recommendations via qualitative and quantitative analysis to identify new growth drivers for the business.
- Metric System Development: Design core metric frameworks aligned with the business value chain, align team objectives and unified measurement standards, and guide the iteration of models and operational strategies.
- Business Improvement Enablement: Evaluate operational strategies with experimental analysis, causal inference and statistical modeling, drive strategy optimization to boost business revenue and decision-making efficiency.
- Agentic Recommendation System: Explore the application of LLMs in data analytics, covering user behavior analysis, crowd segmentation, customer acquisition analysis, growth analytics and profitability analysis, and advance the implementation of the new paradigm of Agentic Data Analysis.
- AI for Data Science: Build AI-powered capabilities for data science teams. Leverage LLMs and Agents to construct automated tools for anomaly attribution, experiment analysis and insight generation; accumulate reusable data science toolkits and standardized analysis frameworks, and build an AI-driven data science team.
Requirements
Minimum Qualification(s)
- Bachelor’s degree in quantitative analysis-related disciplines, including Mathematics, Statistics, Operations Research, Data Science, Computer Science, Economics and Business Analytics.
- Solid foundation in mathematical statistics; proficient in theoretical knowledge of statistics, causal inference, machine learning and big data technologies.
- Skilled in SQL; proficient in at least one programming tool among Python and R.
Preferred Qualification(s)
- Rigorous logical thinking, strong problem-solving skills, excellent communication and cross-team collaboration capabilities, and passion for tackling challenging business problems.
- Solid understanding and hands-on application experience of AI technologies; candidates with proven track records of deploying AI solutions in data analytics scenarios are preferred.

Employment type
Regular