Growth & Business Optimization Topics
Growth strategies, experimentation frameworks, and business optimization. Includes A/B testing, conversion optimization, and growth playbooks.
Design Impact and Metrics
Assess the ability to connect concrete design decisions to measurable product and operational outcomes. Candidates should demonstrate how they select appropriate key performance indicators, define success criteria, specify instrumentation and event tracking, design and run controlled experiments such as A and B testing, and interpret quantitative results alongside qualitative research. Examples include merchant adoption rates, order completion rates, customer satisfaction scores, and operational efficiency gains. The evaluation should cover avoiding vanity metrics, controlling for confounders, using leading and lagging indicators, and translating measurement into prioritization, trade off decisions, dashboards, and continuous monitoring. Deliverables to cite include metric specifications, experiment plans, dashboards, and before and after comparisons that clearly attribute impact to design work.
Hypothesis and Test Planning
End to end practice of generating clear testable hypotheses and designing experiments to validate them. Candidates should be able to structure hypotheses using if change then expected outcome because reasoning ground hypotheses in data or qualitative research and distinguish hypotheses from guesses. They should translate hypotheses into experimental variants and choose the appropriate experiment type such as A and B tests multivariate designs or staged rollouts. Core skills include defining primary and guardrail metrics that map to business goals selecting target segments and control groups calculating sample size and duration driven by statistical power and minimum detectable effect and specifying analysis plans and stopping rules. Candidates should be able to pre register plans where appropriate estimate implementation effort and expected impact specify decision rules for scaling or abandoning variants and describe iteration and follow up analyses while avoiding common pitfalls such as peeking and selection bias.