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Goldman Sachs Interview Questions

A/B Testing

Algorithms

Analytics

Machine Learning

Probability

Product Metrics

Python

Statistics

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The probability that it will rain tomorrow is dependent on whether or not it is raining today and whether or not it rained yesterday.

If it rained yesterday and today, there is a 20% chance it will rain tomorrow. If it rained one of the days, there is a 60% chance it will rain tomorrow. If it rained neither today nor yesterday, there is a 20% chance it will rain tomorrow.

Given that it is raining today and that it rained yesterday, write a function <code>rain_days</code> to calculate the probability that it will rain on the nth day after today.

Example:

Input:

<pre tabindex="0" class="chroma"><code>n=5
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def rain_days(n) -&gt; 0.39968
</code></pre>

Given that it is raining today and that it rained yesterday, write a function to calculate the probability that it will rain on the nth day after today.

Rain in N Days

Suppose we have a binary classification model that classifies whether or not an applicant should be qualified to get a loan.

Because we are a financial company, we have to provide each rejected applicant with a reason why.

Given we don’t have access to the feature weights, how would we give each rejected applicant a reason why they got rejected?

In the context of a loan eligibility model, what does a partial dependence plot represent?

In the context of a loan eligibility model, what does a partial dependence plot represent?

Rejection Reason

How do we give each rejected applicant a reason why they got rejected?

Let’s say that your company is running a standard control and variant AB test on a feature to increase conversion rates on the landing page. The PM checks the results and finds a .04 p-value.

How would you assess the validity of the result?

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