Choose an app you like and we will focus on this for the rest of the interview. Say that users are suddenly dropping off from using the app (they still have an account but stop using it). How would you research the basis for this churn? Then there were additional follow-on technical questions related to steps in looking at different types of data (log data, surveys, integrating qualitative data, sampling/weighting, reaching out to users for feedback when they are no longer using the app, survey development - questions, etc., trade-offs in response analysis choices (e.g., means vs aggregating percent pos/neg, how to prioritize findings, how to present results to stakeholders, what to do if some factor you cannot measure is the reason for churn, and what to do if you need to get some kind of results fast (e.g., like in 2 days and there is no time to conduct a survey). From my interview, Meta heavily focuses on survey data and user log data and producing simple analyses with great rigor (less focus on 'niche' advanced analytics like network analysis or multilevel modeling) so be prepared to show preciseness, in depth knowledge of survey development and analysis and user log data. Do not waste your time preparing answers using advanced analytical approaches.
Quantitativ Interview Questions
10,159 quantitativ interview questions shared by candidates
The difference of Simple and Compound Interest
describe background research
technical questions can be really tricky
First round: What are the assumptions of logistic regression? Why is the logistic regression model considered linear? Online Assessment: 75 min for 5 programming questions, 4 were mathematics and statistics questions, and 1 was a data analysis question. The programming questions consisted of multiple choice choose the R code chunk that produces this given data result. No DSA leetcode style questions were asked. Final Round: – 1st interview - Can you tell me about a time when you had to communicate something complex to layman stakeholders? Can you tell me about a time when you dealt with a conflict in a team? – 2nd interview - Questions about decisions made during feature selection. Questions about handling class imbalance in the models used.
Why is some machine learning algorithm considered a black box 5 assumptions of linear regression What are some ways for optimization
Self-introduction. Describe a project you've worked on. What programming language do you use? What are the assumptions for linear regression? What is overfitting? How to avoid overfitting?
Write down a code to produce an ordered sequence
tell something about yourself and why you choose this position
Do you know how to implement logistic regression?
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