Motivation for Meta's Mission Questions
Explores why a candidate wants to work at Meta, how their personal and professional motivations align with Meta's mission and values, and how they would contribute to Meta's goals. Addresses authenticity, long-term alignment, passion for the product and impact, cultural fit, and the ability to articulate a compelling narrative.
MediumBehavioral
0 practiced
Tell me about a time you had to build trust with a cross-functional team that initially questioned your recommendations. As a BI analyst at Meta, what practical steps did you take to earn credibility, improve adoption, and ensure your mission-aligned insights were acted upon?
HardTechnical
0 practiced
Design an evaluation to test whether Meta's recommendation algorithms improve 'meaningful interactions'. Describe required data pipelines, counterfactual or experimental approaches, instrumentation needs, offline and online evaluation metrics, and how to scale this evaluation across millions of users while controlling for confounders.
HardTechnical
0 practiced
You are asked to lead a cross-functional initiative to shift company KPIs away from short-term engagement toward mission-oriented outcomes. As the lead BI analyst, outline a high-level strategy including stakeholders to engage, a change-management plan, experiments to demonstrate feasibility, how to measure adoption, anticipated resistance, and mitigation strategies.
MediumTechnical
0 practiced
Meta operates globally with diverse cultures and regulations. How would you ensure a mission-centered metric like 'meaningful interactions' is comparable across regions while respecting localization differences and privacy constraints? Describe normalization, local adjustments, and how you'd present regional uncertainty to leadership.
MediumTechnical
0 practiced
How would you measure whether your BI work contributes to long-term societal impact (for example, bringing people closer together) beyond short-term engagement metrics? Propose a measurement plan including leading indicators, lagging outcomes, data sources (surveys, external partners), and evaluation cadence.
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