Applied Research Scientist, World Model at Zillow Group · Remote (United States)
Everything on this page is labelled by where it comes from: questions candidates reported on JobRipe, JobRipe's own preparation guide, and the job ad itself. Nothing here is made up about the company.
Read from this job's ad: what it asks for, so you can prepare one real example for each.
Machine Learning
A time you used Machine Learning: the problem, what you did yourself, and what happened.
From the ad's requirements
Python
A time you used Python: the problem, what you did yourself, and what happened.
From the ad's requirements
PyTorch
A time you used PyTorch: the problem, what you did yourself, and what happened.
From the ad's requirements
Statistics
A time you used Statistics: the problem, what you did yourself, and what happened.
From the ad's requirements
Education
Your studies or training, or the equivalent experience if the ad allows it.
The ad says: “PhD in Computer Science, Machine Learning, Statistics, Economics, or a related quantitative field.”
From the ad's requirements
Demonstrated ability to turn large-scale user behavior data into structured demand representations (utility or willingness-to-pay estimation
An example from your own work that shows this.
From the ad's requirements
Practical causal inference in observational settings (quasi-experiments, instruments, policy discontinuities), and judgment about when a lea
An example from your own work that shows this.
From the ad's requirements
Proven record of delivering on open-ended research projects, with the ability to scope ambiguous problems and provide technical leadership t
An example from your own work that shows this.
From the ad's requirements
Candidate reports: questions verified applicants typed in their interview reviews on JobRipe, shown as written.
No candidate has shared interview questions for Zillow Group on JobRipe yet. Use the job ad and our guide below.
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We show an employee rating once Zillow Group has at least 5 published verified reviews. Company page
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Our editorial preparation guide: typical questions for the field, not reports from candidates.
Why they ask: Rigour.
How to answer: Metric choice, baselines, offline vs online tests.
Why they ask: Communication.
How to answer: Problem, approach, impact, limits.
Why they ask: Machine Learning is named in the posting.
How to answer: One concrete project, one honest lesson.
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Why they ask: Python is named in the posting.
How to answer: One concrete project, one honest lesson.
Why they ask: Opener to see how you frame your story.
How to answer: Two minutes: current role, one achievement that matches this job, why this company now.
Why they ask: Checks ownership and depth.
How to answer: Use situation, action, result. Quantify the result.
Why they ask: Tests accountability and learning.
How to answer: Pick a real failure, own your part, show the fix and what changed afterwards.
Why they ask: Checks judgement under pressure.
How to answer: Describe a concrete framework and one example of a trade-off you made.
Questions to ask them