Machine Learning Engineer, Global Payment Decision Science - USDS

Remote, USA Full-time
About the position The Machine Learning Engineer position within TikTok's Global Payment Decision Science team focuses on designing and building advanced data science and algorithm solutions to enhance payment services. This role is crucial for optimizing user experience and ensuring secure payment transactions through the application of machine learning technologies. Responsibilities • Design and build advanced data science and algorithm solutions to solve business problems such as fraud prevention and user experience optimization. • Understand the payment business and translate business problems into data science and algorithm solutions. • Apply cutting-edge machine learning technologies in various payment business use cases. • Own the end-to-end model solution lifecycle and collaborate with stakeholders to deliver business impact. • Improve modeling infrastructures, labels, features, and algorithms towards robustness, automation, and generalization. • Enhance risk machine learning excellence in privacy/compliance, interoperability, risk perception, and analysis. Requirements • Bachelor's degree or above in Computer Science, Statistics, Mathematics, or related majors. • Proficient in data processing and analytics in a big data environment using SQL, Hive, Spark, etc. • Mastery of at least one prevalent programming language for data science, such as Python, Java, or Scala. Nice-to-haves • Experienced in Machine Learning and Deep Learning algorithms. • 3 years of machine learning-related experience, preferably in trust & safety, risk management, or compliance. Benefits • Medical, dental, and vision insurance from day one. • 401(k) savings plan with company match. • Paid parental leave. • Short-term and long-term disability coverage. • Life insurance. • Wellbeing benefits. • 10 paid holidays per year. • 10 paid sick days per year. • 17 days of Paid Personal Time, prorated upon hire with increasing accruals by tenure. Apply tot his job
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