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Drs Technologies, Inc. Interview Questions

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Machine Learning

Bank Fraud Model

Lasso vs Ridge

Rebalancing outcome probabilities for a classifier on imbalanced data.

Rebalance Probabilities

Bagging vs Boosting

Loan Model

Data Structures & Algorithms

Swap Variables

This question requires the implementation of the Fibonacci sequence using three different methods: recursively, iteratively, and using memoization.

Implementing the Fibonacci Sequence in Three Different Methods

Write a class 'SinglyLinkedList' that implements the interface required for a Singly Linked List.

Singly Linked List

Write a function that returns the number of triplets in the array that sum to `k`.

Triplet Counting

Given a singly linked list, reverse the list starting from index k.

Reverse List Starting at Index K

Behavioral

Describing a data project and its challenges

Hurdles In Data Projects

Strategically resolving misaligned expectations with stakeholders for a successful project outcome

Stakeholder Communication

How to present complex data insights with clarity and adaptability tailored to a specific audience

Presentations and Insights

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

Your Strengths and Weaknesses

How would you answer when an Interviewer asks why you applied to their company?

Why Do You Want to Work With Us

Analytics

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

Analyzing Churn Behavior

How would you approach improving the quality of airline data?

Addressing Data Quality Issues

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

Retention Rate Disparity

Statistics

Covariance vs Correlation

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

Bootstrapping Samples

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.

What’s the difference between Lasso and Ridge Regression?

Under what circumstances is it recommended to use Lasso regression over Ridge regression?

Under what circumstances is it recommended to use Lasso regression over Ridge regression?

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

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

The Fibonacci sequence is a series of numbers in which each number is the sum of the two preceding ones, usually starting with 0 and 1. It is often used in algorithm examples, and is defined by the following formula: F(n) = F(n-1) + F(n-2), with F(0) = 0 and F(1) = 1.

Your task is to implement the Fibonacci algorithm in three different methods:
1. Recursively
2. Iteratively
3. Using Memoization

Example 1:

Input:

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

Output:

<pre tabindex="0" class="chroma"><code>fibonacci(n) -&gt; 5
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>n = 10
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>fibonacci(n) -&gt; 55
</code></pre>

The Fibonacci sequence starts as follows: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55…

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

Let’s say you’re a senior data analyst and your team is tasked with addressing data quality issues for an airline company. Customer records, booking details, and flight schedules often have missing fields, duplicate entries, or incorrect data.

How would you approach improving the quality of this data and ensuring it remains clean and reliable over time?

You are given a dictionary with two keys <code>a</code> and <code>b</code> that hold integers as their values.

Without declaring any other variable, swap the value of <code>a</code> with the value of <code>b</code> and vice versa.

Note: Return the dictionary after editing it.

Example:

Input:

<pre tabindex="0" class="chroma"><code>numbers = {
 &#39;a&#39;:3,
 &#39;b&#39;:4
}
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def swap_values(numbers) -&gt; {&#39;a&#39;:4,&#39;b&#39;:3}
</code></pre>

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

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>

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

What are the key differences between classification models and regression models?

What is the MOST important difference between regression and classification models?

What is the MOST important difference between regression and classification models?

Classification vs Regression

Classification and Regression

Let’s say you work for an e-commerce company. Vendors can send products to the company’s warehouse to be listed on the website. Users are able to order any in-stock products and submit returns for refunds if they’re not satisfied.

The front end of the website includes a vendor portal that provides sales data in daily, weekly, monthly, quarterly, and yearly intervals.

The company wants to expand worldwide. They put you in charge of designing its end-to-end architecture, so you need to know what significant factors you’ll need to consider. What clarifying questions would you ask?

What kind of end-to-end architecture would you design for this company (both for ETL and reporting)?

How would you design a data warehouse for a e-commerce company looking to expand internationally?

International e-Commerce Warehouse

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 have been given an array of integers, and a query number <code>k</code>. Your task is to write a function that finds all the triplets in the array that sum up to the query number <code>k</code>.

Let’s illustrate this with some examples:

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>nums = [1, 2, 3, 4, 5]
k = 6
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>count_triplets(nums, k) -&gt; 1
</code></pre>

In this case, there is only one triplet that sums up to 6: (1, 2, 3).

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>nums = [10, 10, 20, 30, 40]
k = 60
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>count_triplets(nums, k) -&gt; 3
</code></pre>

In this case, there are three triplets that sum up to 60: (10, 10, 40), (10, 20, 30), and (10, 20, 30).
The value 10 can be retrieved as <code>nums[0]</code> or <code>nums[1]</code>, therefore resulting in two triplets with the same value.

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

When an interviewer asks you a question along the lines of:

<ul>
<li>Why did you apply to our company?</li>
<li>What are you looking for in your next job?</li>
<li>What makes you a good fit for our company?</li>
</ul>

How should you respond?

When asked 'What are you looking for in your next job?' in an interview, how can you tie the company's employee benefits into your response?

When asked 'What are you looking for in your next job?' in an interview, how can you tie the company's employee benefits into your response?

Why Do You Want to Work With Us I

How can company values be used effectively in an interview when asked 'What makes you a good fit for our company?'

How can company values be used effectively in an interview when asked 'What makes you a good fit for our company?'

Why Do You Want to Work With Us II

When responding to the question 'Why did you apply to our company?' during an interview, what aspect should you highlight?

When responding to the question 'Why did you apply to our company?' during an interview, what aspect should you highlight?

Why Do You Want to Work With Us III

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

Describe a data project you worked on. What were some of the challenges you faced?

Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?

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?

How to model merchant acquisition in a new market?

Merchant Acquisition

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?

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 is the Martingale strategy and how might it be used in online advertising?

The use of Martingale strategy for finance and online advertising

Martingale Strategy

Probability

Singly-linked lists are a type of linked list where nodes have only one pointer: A pointer to the next element. Create a class <code>SinglyLinkedList</code> along with its methods:

<ul>
<li><code>add_head(element)</code> - adds an element to the head of the list. This becomes the new head of the linked list.</li>
<li><code>add_tail(element)</code> - adds an element to the tail of the list. This becomes the new tail of the linked list.</li>
<li><code>remove_head() -&gt; element</code> - removes the head and returns the value of the removed element.</li>
<li><code>remove_tail() -&gt; element</code> - removes the tail and returns the value of the removed element.</li>
<li><code>__contains__(item) -&gt; bool</code> - checks if the item is inside the list. Returns <code>True</code> if it is inside the list and returns <code>False</code> if not. This method is implemented to allow the use of Python’s <code>in</code> operator.</li>
<li><code>__getitem__(index)</code> - returns the element at the specified index. Raises an <code>IndexError</code> if the index is out of bounds. This method is implemented to allow the use of Python’s <code>[]</code> operator.</li>
<li><code>__len__()</code> - returns the length of the <code>SinglyLinkedList</code>.</li>
</ul>

How comfortable are you presenting your insights?

You’re given the head node of a singly linked list and a natural number <code>k</code>. <code>k</code> will always be less than or equal to the length of the list, such that <code>k &lt;= len(SinglyLinkedList)</code>. Write a function <code>reverse_at_position(head, position)</code> that reverses the linked list starting from the index <code>k</code> (the index starts at 0) to the end of the list.

A class <code>Node</code> is defined as the following:

<pre tabindex="0" class="chroma"><code>class Node:
 def __init__(self, element, next=None):
 self.element = element
 self.next = next
</code></pre>

Note: After the reverse operations, you must update the new head node using the <code>update_head(node)</code> function (especially in the case where <code>n = 1</code>). The tail’s next node will be <code>None</code>.

Example:

Input:

<pre tabindex="0" class="chroma"><code>reverse_at_position([1, 2, 3, 4, 5, 6], 2)
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>[1, 2, 3, 6, 5, 4]
</code></pre>

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

Drs Technologies, Inc. Interview Questions

Drs Technologies, Inc. Interview Guides

Drs Technologies, Inc. Interview Questions

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