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Offerzen Interview Questions

A/B Testing

Algorithms

Analytics

Machine Learning

Probability

Product Metrics

Python

Statistics

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You work as a data engineer for a city-wide bicycle rental company. The company wants to predict future bicycle rental volumes using historical data collected from multiple sources, such as station logs, weather files, and event schedules. You are given access to raw source files that are updated daily.

How would you design an end-to-end data pipeline to prepare and serve data for downstream predictive modeling of bicycle rental demand?

Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes.

Bicycle Rental Data Pipeline

Batch & Stream Processing

Suppose there exists a new airline named Jetco that flies domestically across North America. Jetco recently had a study commissioned that tested the boarding time of every airline, and it came out that Jetco had the fastest average boarding times of any airline.

What factors could have biased this result, and what would you look into?

What could potentially bias the average boarding time in favor of an airline that only flies domestically?

What could potentially bias the average boarding time in favor of an airline that only flies domestically?

Boarding Times Bias

A new airline came out as the fastest average boarding times compared to other airlines.  What factors could have biased this result and what would you look into?

You are testing hundreds of hypotheses with many t-tests. What considerations should be made?

What is a key disadvantage of the F-test when compared to multiple t-tests?

What is a key disadvantage of the F-test when compared to multiple t-tests?

Hundreds of Hypotheses

You are testing hundreds of hypotheses with many t-tests. What considerations should be made?

Question	Topic	Difficulty	Ask Chance
Bicycle Rental Data Pipeline	Batch & Stream Processing	Medium	Very High
Boarding Times Bias	Statistics	Medium	Very Low
Hundreds of Hypotheses	Statistics	Medium	Very Low