The Business Risk Integrated Control (BRIC) team is missioned to:
- Protect ByteDance users, including and beyond content consumers, creators, advertisers;
- Secure platform health and community experience authenticity;
- Build infrastructures, platforms and technologies, as well as to collaborate with many cross-functional teams and stakeholders.
The BRIC team works to minimize the damage of inauthentic behaviors on ByteDance platforms (e.g. TikTok, CapCut, Resso, Lark), covering multiple classical and novel community and business risk areas such as account integrity, engagement authenticity, anti spam, API abuse, growth fraud, live streaming security and financial safety (ads or e-commerce), 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 evolvement of a phenomenal product eco-system. The work needs to be fast, transferrable, while still down to the ground to making quick and solid differences.
- Enable and contribute to establishing fast and continuous threat response, in partnership with risk data mining engineers and product partners, by building advanced analytical tools and derive data insights.
- Analyze security and product data to uncover latest attacker techniques and abuse vectors. Generalize them into data mining opportunities in product features; modelling signals, labels and algorithms; measurement and process effectiveness.
- Enable and contribute to establishing robust, powerful and privacy-aware automated defense, in partnership with risk data mining engineers, by leveraging risk systems, machine learning tools and business resources to build and improve risk control rules, models and products.
- Abstract and build risk measurements that best connect risk research, risk operation, risk defense and business/community health with data stories, drive strategic and tactical risk control roadmaps with such metrics collaboratively with product/business/engineering stakeholders.
- Bachelor, master or PhD degree in an advanced field of technology or management or other equivalent majors. E.g. Computer science, statistics, internet security, finance.
- Proficiency in data analysis and statistical analysis tools such as SQL, R and Python.
- Ability to think critically, to properly communicate problem challenges/solutions to, and to hold (as well as to be held) accountabilities on cross-functional partners in a clear, concise and timely manner.
- PhD degree in Computer Science or Statistics. Publications in top academic conferences on relevant data mining topics about social communities and content platforms (e.g. Botnet, interest group mining, fraud detection).
- 2+ years of industry experience (internship included) in predictive analytics and/or statistical modelling.
- Strong ownership (proactivity, initiative, followthrough) to surface and solve open-ended problems against abusive attackers with adversarial nature. Self-starter/go-getter.
- Experience in risk analytics in multiple areas: account take over, fraud, spam, abuse and etc. OR strong experience in data/statistical analysis on general internet or financial business. OR deep understanding about modern Machine Learning theory, applications, industry best practice.
- Consistency at exercising data science best practice in modern internet industry or academic world. Persistence in reasoning with data, interpreting data objectively, and making data-driven decisions.