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TikTok

TikTok Shop - Data Scientist - US Operations

TikTok, Seattle, Washington, us, 98127

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TikTok Shop - Data Scientist - US Operations

Responsibilities The Global e-commerce Data Science team aims to maximize the efficiency of e-commerce transactions, lead product decision-making iterations, and achieve sustainable growth in revenue with data and scientific methods, through quantitative techniques such as mathematical statistics and machine learning. This role for our US Operations team will be providing data solutions to E-commerce Operations teams to help solve interesting challenges for our Creator & Merchants. What you will do : Develop comprehensive analytics framework and product metrics to proactively track, attribute, and identify opportunities to improve TikTok Shop's business/user product; Identify key trends, patterns, and opportunities to drive e-commerce business growth and optimize business/user product offerings; Monitor key performance indicators and develop metrics framework to help measure cross-functional efforts in a quantitative way; Conduct A/B testing and experimentation, providing actionable insights to stakeholders for improving or iterating product features; Partner closely with key stakeholders to optimize overall product adoption and performance-driven growth; Design and implement reporting dashboards and data pipelines to deliver insights and enhance workflows of internal teams. Qualifications Minimal Qualifications: Bachelor's degree in Mathematics, Statistics, Computer Science, or Analytics; At least 3 years of experience in Data Science; Proficient in SQL, Python or R, with expertise in measurement, modeling, or optimization; Proactive, self-driven, and impact-oriented mindset; Ability to work with cross-functional teams in a fast-paced environment. Preferred Qualifications: Advanced Degree (MS, PhD.) in Mathematics, Statistics, Analytics, etc.; Excellent communication skills, open-mindedness, and positive critical thinking; Experience in e-commerce or online marketplaces is strongly preferred.

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