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Machine Learning

Bank Fraud Model

Rebalancing outcome probabilities for a classifier on imbalanced data.

Rebalance Probabilities

Bagging vs Boosting

Loan Model

How to model merchant acquisition in a new market?

Merchant Acquisition

Statistics

Covariance vs Correlation

What does it mean to "bootstrap" a data set?

Bootstrapping Samples

Behavioral

Describing a data project and its challenges

Hurdles In Data Projects

What do you tell an interviewer when they ask you what your strengths and weaknesses are?

Your Strengths and Weaknesses

Analytics

How would you present the performance of each subscription to an executive?

Analyzing Churn Behavior

Let's say you work at Facebook and you're analyzing churn on the platform.

Retention Rate Disparity

Business Case

As a data scientist at a mortgage bank, how would you approach building a predictive model for loan default risk?

Risk Model for a Mortgage Bank

Let’s say that you work at a bank that wants to build a model to detect fraud on the platform.

The bank wants to implement a text messaging service in addition that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.

How would we build this model?

Let’s say that you work at a bank that wants to build a model to detect fraud on the platform.

The bank wants to implement a text messaging service that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.

Which statement is true?

Let's say that you work at a bank that wants to build a model to detect fraud on the platform.

The bank wants to implement a text messaging service that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.

Which statement is true?

What is the difference between covariance and correlation? Provide an example.

Let’s say we’re comparing two machine learning algorithms. In which case would you use a bagging algorithm versus a boosting algorithm? 

Give an example of the tradeoffs between the two.

In machine learning, when would you use a bagging algorithm over a boosting algorithm?

In machine learning, when would you use a bagging algorithm over a boosting algorithm?

Bagging vs. Boosting

Tell me about a data project that didn’t go the way you expected. What did you set out to do, what surprised you, and how did you handle it?

How does random forest generate the forest? Additionally, why would we use it over other algorithms such as logistic regression?

What happens when you average the output on multiple decision trees?

What happens when you average the output on multiple decision trees?

Average Trees

Random Forest Explanation

Imagine you are asked to build a machine learning model to decide new loan approvals for a
financial firm. You ask the data department in the company for a subset of data to get started
working on the problem. The data includes different features about applicants such as age,
occupation, zip code, height, number of children, favorite color, etc. You decide to build
multiple machine learning models to test out different ideas before settling on the best one.

How would you explain the bias-variance tradeoff with regards to
building and choosing a model to use?

Bias vs. Variance Tradeoff

How would you handle the data preparation for building a machine learning model using imbalanced data?

Addressing imbalanced data in machine learning through carefully prepared techniques.

Data Preparation for Imbalanced Data

Given a string, write a function to determine if it is palindrome or not.

Note: A palindrome is a word/string that is read the same way forward as it is backward, e.g. <code>&#39;reviver&#39;</code>, <code>&#39;madam&#39;</code>, <code>&#39;deified&#39;</code> and <code>&#39;civic&#39;</code> are all palindromes, while <code>&#39;tree&#39;</code>, <code>&#39;music&#39;</code> and <code>&#39;person&#39;</code> are not palindromes.

Example:

Input:

<pre tabindex="0" class="chroma"><code>word1 = &#34;tree&#34;
word2 = &#34;radar&#34;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def is_palindrome(word1) -&gt; False
def is_palindrome(word2) -&gt; True
</code></pre>

Given a string, write a function to determine if it is palindrome or not.

String Palindromes

Data Structures & Algorithms

Let’s say you work for a bank that gives out personal loans. Your co-worker develops a model that takes in customer inputs and returns if a loan should be given or not.

<ol>
<li>What kind of model did the co-worker develop? 
</li>
<li>Another co-worker thinks they have developed a better model to predict defaults on the loans. Given that personal loans are monthly installments of payments, how would you measure the difference between the two credit risk models within a timeframe? 
</li>
<li>What metrics would you track to measure the success of the new model?</li>
</ol>

Let’s say you’re trying to calculate churn for a subscription product.

You noticed that of all customers that bought subscriptions in January 2020, about 10% of them canceled their membership before their next cycle on February 1st.  

If you assume that your new customer acquisition is uniform throughout each month and that customer churn goes down by 20% month over month, what’s the expected churn rate in March for all customers that bought the product since January 1st?

Expected Churn

Product Sense & Metrics

Imagine you run a pizza franchise, and you run into a problem with a lot of no-shows after customers place their order.

What features would you include in a model to try to predict a no-show?

Pizza No Show

Let’s say you work as a data scientist at DoorDash. 

You are tasked to build a machine learning system that minimizes missing or wrong orders placed on the app.

How would you go about designing this system?

Minimize Wrong Orders

Let’s say that you work for a company like Netflix. Netflix has two pricing plans: $15/month or $100/year.

Let’s say an executive at the company wants you to analyze the churn behavior of users that subscribe to either plan.

What kinds of metrics / graphs / models would you build to help give the executive an over-arching view of how the subscriptions are performing?

Let’s say that you’re a data scientist at DoorDash.

How would you build a model to predict which merchants the company should go after for acquisition when entering a new market?

Suppose we are training a binary classification algorithm. 

The outcome is imbalanced, with 99.8% of individuals in our sample having an outcome value of 0, and 0.2% having an outcome value of 1. To build a model, we down-sample the training data using a random sample of 1% of the individuals who have an outcome of 0 and keeping all individuals with an outcome of 1.

Now we train the model on the smaller sample and build a binary classifier that predicts the probability of an individual with a value of 1.

How would we then adjust our output probabilities to use this model on the total, imbalanced population?

When an interviewer asks a question along the lines of:

<ul>
<li>What would your current manager say about you? What constructive criticisms might he give?</li>
<li>What are your three biggest strengths and weaknesses you have identified in yourself?</li>
</ul>

How would you respond?

When asked about your strengths in an interview, what is an effective way to respond?

When asked about your strengths in an interview, what is an effective way to respond?

Your Strengths and Weaknesses I

Which of the following is an acceptable strategy when discussing weaknesses in an interview?

Which of the following is an acceptable strategy when discussing weaknesses in an interview?

Your Strengths and Weaknesses II

Imagine you are trying to track the position of a moving car using noisy GPS data, how would you explain the Kalman filter to someone with no technical background?

Use a real-world analogy to make your explanation accessible.

Explain the Kalman filter in simple, real-world terms.

Kalman Filter in GPS tracking

Forecasting & Time Series

What does it mean to “bootstrap” a data set?

Give an example of a time when it would be appropriate to bootstrap a smaller sample(s) from a larger data set.

What are the advantages and disadvantages of doing so?

Let’s say you work at Facebook and you’re analyzing churn on the platform.

<ol>
<li>How would you investigate if a disparity exists in retention on different Facebook products?</li>

<li>How would you investigate the causes of such a disparity?</li>
</ol>

How would you explain the bias variance tradeoff in machine learning to a high school student?

Replaced by QUESTION 791 - Should be deleted

Bias Variance Tradeoff

As a data scientist at a mortgage bank, how would you approach building a predictive model for loan default risk?

What does the backpropagation algorithm do in the context of neural networks? What is the informal intuition behind the algorithm? What are some drawbacks of the algorithm compared to other optimization methods?

Bonus: Formally derive the backpropagation algorithm and prove that it does what it claims to do.

Calculate Moving Average	SQL	Easy
Predict Customer Churn	Machine Learning	Medium
A/B Test Significance	Statistics	Medium
Optimize Query Performance	SQL	Hard
Feature Importance Analysis	Machine Learning	Medium
Clean Missing Data	Python	Easy
Neural Network Architecture	Deep Learning	Hard
Calculate Cohort Retention	SQL	Medium
Bayesian Probability	Statistics	Easy
Recommend Similar Products	Machine Learning	Hard

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