Growth & Business Optimization Topics
Growth strategies, experimentation frameworks, and business optimization. Includes A/B testing, conversion optimization, and growth playbooks.
Experimentation and Product Validation
Designing and interpreting experiments and validation strategies to test product hypotheses. Includes hypothesis formulation, experimental design, sample sizing considerations, metrics selection, interpreting results and statistical uncertainty, and avoiding common pitfalls such as peeking and multiple hypothesis testing. Also covers qualitative validation methods such as interviews and pilots, and using a mix of methods to validate product ideas before scaling.
Data Driven Strategy and Experimentation
Covers how to balance quantitative evidence and qualitative judgment when making product and technical decisions. Topics include recognizing limits of observational data, designing and interpreting split testing and cohort analysis, causal inference fundamentals, measurement frameworks and success metrics, statistical power and sample size considerations, making decisions with incomplete or noisy data, prioritizing experiments for strategic bets, building analytics and data science partnerships, teaching non data stakeholders statistical thinking, and cultivating an organizational culture of experimentation and learning where hypothesis driven work informs prioritization.
Growth Prioritization Frameworks
Core frameworks and techniques used to prioritize growth projects and experiments. Includes qualitative matrices such as impact and effort mapping and value versus complexity, and quantitative scoring models such as RICE scoring spelled out as reach times impact times confidence divided by effort. Candidates should understand how to estimate reach, impact magnitude, confidence or uncertainty, and required effort; consider sample size and statistical confidence when prioritizing experiments; assess strategic alignment with company goals and resource constraints; and communicate tradeoffs clearly. Interview preparation includes practicing ranking and scoring hypothetical initiatives, explaining assumptions and sensitivity to inputs, and justifying prioritization decisions under time or resource constraints.
A/B Testing and Optimization Methodology
Discuss your experience designing and running A/B tests on content elements: headlines, formats, messaging, calls-to-action, visual design, content length, etc. Share specific examples of tests you've run with results and how you implemented learnings. Discuss statistical significance and proper experimental design. Show how you prioritize testing opportunities and build a testing roadmap.
Feature Success Measurement
Focuses on measuring the impact of a single feature or product change. Key skills include defining a primary success metric, selecting secondary and guardrail metrics to detect negative side effects, planning measurement windows that account for ramp up and stabilization, segmenting users to detect differential impacts, designing experiments or observational analyses, and creating dashboards and reports for monitoring. Also covers rollout strategies, conversion and funnel metrics related to the feature, and criteria for declaring success or rollback.
Experimentation Philosophy & Test Design
Articulate your philosophy on experimentation and why rigorous testing matters for growth. Discuss your approach to hypothesis generation, test design, metric selection, and learning from results. Walk through a detailed example: What were you testing? Why did you think it would work? What did you actually find? How did you apply the learning? Discuss how you've used experimentation to challenge assumptions and drive strategic decisions. Mention statistical concepts you consider (power, sample size, significance).
A and B Test Design
Designing and running A and B tests and split tests to evaluate product and feature changes. Candidates should be able to form clear null and alternative hypotheses, select appropriate primary metrics and guardrail metrics that reflect both product goals and user safety, choose randomization and assignment strategies, and calculate sample size and test duration using power analysis and minimum detectable effect reasoning. They should understand applied statistical analysis concepts including p values confidence intervals one tailed and two tailed tests sequential monitoring and stopping rules and corrections for multiple comparisons. Practical abilities include diagnosing inconclusive or noisy experiments detecting and mitigating common biases such as peeking selection bias novelty effects seasonality instrumentation errors and network interference and deciding when experiments are appropriate versus alternative evaluation methods. Senior candidates should reason about trade offs between speed and statistical rigor plan safe rollouts and ramping define rollback plans and communicate uncertainty and business implications to technical and non technical stakeholders. For developer facing products candidates should also consider constraints such as small populations cross team effects ethical concerns and special instrumentation needs.