Analyze Product Launch and Creator Engagement

Imagine you are interviewing for a Data Scientist intern position at a short-video platform. Base your answers on a recent data or machine-learning project listed on your resume. Respond to the prompts below:

1. Project and technical deep dive: Give a short overview of the project, the business issue it addressed, the data involved, and your contribution. Then walk through your data preprocessing steps, how you managed the bias-variance tradeoff, how you chose the model or analysis performance metrics, and how those metrics tied back to the business goal.
2. Product launch research case: Imagine the company decides to release the product or feature you developed. How would you structure the research plan for both before and after launch? Cover hypotheses, user segments, data needs, experiment or observational design, success metrics, guardrail metrics, and decision criteria.
3. Creator engagement case: Suppose you have a metric like the count of new users who signed up and posted at least once on the same day. The company aims to boost creator engagement. How would you investigate this problem and suggest next steps? Overview: This question tests a candidate's applied machine learning and data science skills, including model building and evaluation, data preprocessing, business-aligned metric design, experimental and observational analysis, and creator engagement/product analytics.