Improve TikTok's Algorithm for Diverse Content Discovery

Q: Improve TikTok's Algorithm for Diverse Content Discovery

This question evaluates product analytics and experimentation competencies, including hypothesis formulation, metric selection, A/B test design and power calculation, quantitative impact estimation, and product sense for improving content discovery on a short-form video platform.

Q: How do I approach Analytics & Experimentation interview questions?

Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

Question

Product Feedback and Experimentation Case

Context

You are a frequent TikTok user interviewing for a Data Scientist (Analytics & Experimentation) role. You are asked to provide product feedback with measurable, experiment-ready improvements.

Prompt

Identify 3–5 aspects of TikTok you are dissatisfied with (e.g., search, content relevance, safety, creator tools, monetization).
For each aspect, propose one concrete improvement:
- What exactly would you change or add?
- How would it integrate into the user experience?
Justify expected user and business impact. State hypotheses, trade-offs, and risks.
Outline how you would measure success post-launch:
- Primary, secondary, and guardrail metrics
- Experiment design (randomization unit, power, segments, duration)

Keep answers concise but analytical; use examples and metrics.

Improve TikTok's Algorithm for Diverse Content Discovery

Product Feedback and Experimentation Case

Context

Prompt

Solution

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