Scaling Research Initiatives Across Teams Questions
Develop frameworks for scaling research across multiple product teams, platforms, or user segments. Discuss how to structure research so it can grow without proportional increase in resources. Address how to balance centralized research strategy with decentralized team autonomy. Explain how to build research infrastructure and processes that scale. Discuss knowledge management and how to leverage research across the organization.
MediumBehavioral
0 practiced
A product team resists adopting central experiment templates and prefers ad-hoc methods. Role-play: how would you influence them to adopt templates while preserving their sense of autonomy and speed? Outline your conversation plan and success signals.
HardTechnical
0 practiced
You need to quantify and attribute business impact of a central research program that influences recommendations, experiments, and product changes across multiple teams. Propose a measurement strategy that combines randomized evaluation, observational causal inference, and qualitative triangulation, and explain how you would attribute partial credit for multi-touch influence.
HardSystem Design
0 practiced
Architect a multi-tenant research platform to support 100 product teams and 10M monthly active users, with strict permissioning, data lineage, compliance tracking, and low-latency dashboard queries. Describe core components, data flow, storage choices, indexing strategies, and partitioning plan to meet scale and compliance.
MediumTechnical
0 practiced
Implement in Python a function normalize_and_aggregate(dataframes, metric_map) that takes a list of pandas DataFrames each with columns ['date', 'team', 'metric_name', 'value'] and a mapping dict that standardizes metric names. The function should return a pivoted DataFrame with rows by date and team and standardized metric columns containing aggregated sums. Specify edge-case handling for missing metrics.
HardTechnical
0 practiced
You need to scale randomized controlled trials across multiple regions with differing baseline conversion rates and traffic volumes. Propose sampling strategies, power calculations by stratum, and sequential monitoring rules (alpha spending or group sequential) to maintain power while controlling Type I error under adaptive enrollment.
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