Staff Data Scientist (Growth)

优缺点分析

角色解读

• Advance to Senior Data Scientist or Lead Data Scientist, taking on more complex models and strategic projects.
• Transition into data science management, leading a team of data scientists and analytics professionals.
• Move into product analytics or marketing analytics leadership, shaping the data-driven culture at Coupang.
• Collaborate with marketing, CRM, and engineering teams to make data-driven decisions on campaigns and promotions.
• Build and deploy machine learning models for customer churn prediction, sales forecasting, and click-through rate estimation.
• Conduct A/B testing and regression analysis to optimize online ad performance and marketing ROI.
• Create and maintain data pipelines to ensure data quality and availability for analysis.
• Strong proficiency in machine learning, with 5+ years of hands-on experience in building and deploying models.
• Expert-level SQL and Python skills for data manipulation, analysis, and model development.
• Ability to design and analyze A/B tests and experiments, with solid statistical knowledge.
• Excellent communication skills in both Mandarin and English to present insights to stakeholders.

申请策略

• Tailor your resume to highlight growth marketing analytics experience, such as customer churn or campaign optimization.
• Research Coupang's Taiwan operations and be prepared to discuss how your skills can contribute to their expansion.
• Emphasize 5+ years of machine learning experience with specific models (e.g., classification, regression, clustering).
• Showcase SQL and Python projects that involved large datasets, data validation, and actionable insights.
• Highlight any experience in A/B testing, hypothesis testing, and statistical analysis.
• Include examples of collaboration with marketing or CRM teams to drive business decisions.
• If lacking, gain experience in A/B testing frameworks and causal inference methods.
• Familiarize with cloud platforms (AWS/GCP) and data engineering (ETL) if not already proficient.

面试指南

• Use the STAR method (Situation, Task, Action, Result) to structure responses, focusing on data-driven decisions and business outcomes.
• For technical questions, walk through your thought process step-by-step, including data preparation, modeling, validation, and deployment considerations.
• How would you design an A/B test for a new marketing campaign? Walk through the process from hypothesis to conclusion.
• Describe a machine learning model you built that had a significant business impact. What challenges did you face?
• How do you handle data quality issues when analyzing large datasets? Give an example.
• Explain how you would measure the effectiveness of a CRM promotion targeting inactive users.
• Can you describe a time when you had to influence a non-technical stakeholder using data? What approach did you take?
• Review causal inference and experiment design fundamentals, as A/B testing is a key part of the role.