Data Scientist,Operations-TikTok Shop

Responsibilities

The commerce data science team aims to maximize the efficiency of commerce transactions through quantitative techniques such as mathematical statistics and machine learning. At the same time, we are committed to building a more diverse, inclusive, candid and efficient working atmosphere. We sincerely invite excellent data scientists to join us in building a first-class commerce platform.

In this role, you will:
1. Provide actionable insights to drive SEA business growth, and improvement of operational efficiency;
2. Leverage data science expertise to design data solutions to solve commerce operation/product problems such as supply growth, seller growth and marketplace efficiency etc.;
3. Partner closely with key stakeholders to monitor, diagnose and optimize overall business performance and health;
4. Provide in-depth analysis with localized suggestions for improvements to align with business goals;
5. Build and improve data infrastructure such as dashboards and self-serve analytics tools to improve efficiency and support decision-making.

Qualifications

Minimum Qualifications:
1. At least 5 years of working experience in data science/data analytics, with a master's degree or above, majoring in quantitative fields (i.e. mathematics, statistics) or business information tech/science/engineering (i.e. Computer Science) fields are preferred;
2. Proficient in SQL, Python or R, and expertise in at least one of the three areas: advanced measurement (e.g AB testing, causal inference or systems with complex trade-offs), modeling, and optimization;
3. Fluent English in both verbal and written business communication, to work with a global team;
4. To succeed in the role, you need to be a proactive, self-driven and impact-driven person;
5. Able to work with cross-functional teams in a fast-paced environment.

Preferred Qualifications:
1. E-commerce or online marketplace work experience is strongly preferred.
2. Overseas business experience or analytical experience is preferred.