Interview
Questions
Google Ads Data Science: Lift, Privacy, and Measurement
Independent spoken practice for advertising-measurement data science: experimental choices, privacy constraints, analytical delivery, and technical influence. Informed by public role responsibilities; not an official Google interview guide.
Practice these questions directly. An editable copy is yours to change; the original stays unchanged.
Give me an example of when your analysis led to a significant change in business strategy or product direction.
You are planning a Google Ads Conversion Lift study, but user-level assignment is unavailable for part of the audience. How would you choose between a user-level and a geographic experiment, and explain what could make the lift estimate misleading?
Google Ads measurement needs to remain useful when less user-level data can be retained. How would you adapt the analysis and explain the trade-offs between privacy, statistical precision, and usefulness to advertisers?
A retail client wants to move budget after one week of strong attributed sales from a channel. How would you compare that evidence with marketing mix modeling and an incrementality test before recommending a change?
Walk me through a data pipeline you designed from scratch. What were the key decisions you made, and what trade-offs did you consider?
How have you explained complex technical findings or model limitations to non-technical stakeholders? Give me a specific example.
Tell me about a time you mentored someone or helped a teammate grow. What was your approach?