Prepare for and practice interview questions from Cigna across topics like Algorithms, Machine Learning, Probability and more.

Cigna Interview Questions

Cigna Interview Guides

Machine Learning

Bagging vs Boosting

What are the logistic and softmax functions? What is the difference between the two?

Softmax vs Logistic

Decision Tree Evaluation

Correlated Features in Forests

Credit Card Fraud Model

Analytics

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

Let's say that you work at TikTok. The goal for the company next quarter is to increase the daily active users metric (DAU).

Best DAU

Sports App Cheater

Statistics

P-value to a Layman

What are the assumptions of linear regression?

Assumptions of Linear Regression

Covariance vs Correlation

Describe linear regression to various audiences with different levels of knowledge.

Explaining Linear Regression to Different Audiences

Multicollinearity in Regression

Write a SQL query to create a histogram of the number of comments per user in the month of January 2020.

Comments Histogram

Get the top 3 highest employee salaries by department

Top Three Salaries

Write a SQL query to count transactions filtered by several criterias.

Count Transactions

Write a query to find all users that were at some point "Excited" and have never been "Bored" with a campaign.

Always Excited Users

Total Spent on Products

Data Pipelines

Design a data pipeline for hourly user analytics.

Data Pipelines and Aggregation

Let's say that you're in charge of getting payment data into your internal data warehouse. 

Payment Data Pipeline

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

International e-Commerce Warehouse

Aggregating and collecting unstructured data.

Unstructured Data Pipeline (ETL)

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

Your Strengths and Weaknesses

Brainteasers

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

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

Why Do You Want to Work With Us

What are the assumptions of linear regression?

Which assumption of the residuals of the standard linear regression model can not be overcome by increasing the sample size?

Regression assumptions

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

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

How would you explain what a p-value is to someone who is not technical?

What does a p-value in a statistical test represent?

What does a p-value in a statistical test represent?

P-value to a Layman I

In a statistical test, how does a low p-value (less than 0.05) influence our decision about the null hypothesis?

In a statistical test, how does a low p-value (less than 0.05) influence our decision about the null hypothesis?

P-value to a Layman II

Let’s say that you’re training a classification model.

How would you combat overfitting when building tree-based models?

Let's say that you're training a classification model.   How would you combat overfitting when building tree-based models?

Overfit Avoidance

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

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

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 work as a data scientist at a bank.

You are tasked with building a decision tree model to predict if a borrower will pay back a personal loan they are taking out.

<ol>
<li>How would you evaluate whether using a decision tree algorithm is the correct model for the problem?</li>

<li>Let’s say you move forward with the decision tree model. How would you evaluate the performance of the model before deployment and after?</li>
</ol>

Given the <code>employees</code> and <code>departments</code> table, write a query to get the top 3 highest employee salaries by department. If the department contains less that 3 employees, the top 2 or the top 1 highest salaries should be listed (assume that each department has at least 1 employee). 

Note: The output should include the full name of the employee in one column, the department name, and the salary. The output should be sorted by department name in ascending order and salary in descending order. 

Example:

Input:

<code>employees</code> table

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>first_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>last_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>department_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<code>departments</code> table

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>employee_nam</code>e</td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>department_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

How would you tackle multicollinearity in multiple linear regression?

Let’s you’re tasked with pitching a new feature for Google Home. Your co-worker comes to you with an idea to build a game feature for Google Home.

How would you go about deciding whether Google should build it?

Game Feature Home

Business Case

How would you explain the concept of linear regression to three different audiences: a child, a first-year college student, and a seasoned mathematician? Ensure your explanations are tailored to the understanding level of each audience.

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>

Swap Variables

Data Structures & Algorithms

Let’s say you have analytics data stored in a data lake. An analyst tells you they need hourly, daily, and weekly active user data for a dashboard that refreshes every hour.

How would you build this data pipeline?

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)?

Let’s say you’re interning at a fintech startup that’s building a platform for online games of chance. During testing, one of your simulations flips a coin 10 times and gets 8 tails and 2 heads.

How would you determine if the coin is fair, and what statistical approach would you use to support your conclusion?

How would you assess if a coin is fair after observing 8 tails in 10 flips?

Fair Coin

Probability

How comfortable are you presenting your insights?

What are the logistic and softmax functions? What is the difference between the two?

What makes them useful for use in logistic regression?

In logistic regression, how is the predicted class determined based on the logistic function?

In logistic regression, how is the predicted class determined based on the logistic function?

Softmax vs Logistic II

What is the key characteristic of the logistic function that makes it useful for mapping continuous values to a probability?

What is the key characteristic of the logistic function that makes it useful for mapping continuous values to a probability?

Softmax vs Logistic I

Let’s say that you’re in charge of getting payment data into your internal data warehouse.

How would you build an ETL pipeline to get Stripe payment data into the database so analysts can build revenue dashboards and run analytics?

You work as a machine learning engineer for a health insurance company. Design a machine learning model, which given a set of health features, classifies if the individual will undergo major health issues or not.

Creating a machine learning model for evaluating a patient's health

Risk Assessment Model

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?

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

Bootstrapping Samples

Let’s say that you’re working at a global trading company and you’re analyzing the price of a particular asset over time. This dataset is super noisy, volatile, and you’re not guaranteed completely accurate data.

How would you analyze this data to ensure there aren’t any discrepancies?

Time Series Discrepancies

Forecasting & Time Series

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

A product manager at Facebook comes to you and tells you that friend requests are down 10%.

What would you do?

You work as a data analyst on a social media platform similar to Facebook. If you observe a 10% decline in friend requests over the last three months, which analysis or metric-tracking approach could aid in uncovering the reasons?

You work as a data analyst on a social media platform similar to Facebook. If you observe a 10% decline in friend requests over the last three months, which analysis or metric-tracking approach could aid in uncovering the reasons?

Friend Requests Down

You work as an ML engineer for a large company. The company wants to implement a machine learning system that utilizes facial recognition to facilitate employee clock-in, clock-out, and access to secure systems. The company also hires temporary contract consultants who need to be able to use the system.

How would you design this system?

Designing a secure and user-friendly facial recognition system for employee management while prioritizing privacy and ethical considerations

Distributed Authentication Model

Let’s say we want to build a new algorithm for Lyft Line.

How should we test it, measure success of the test, and eventually roll it out?

Building Lyft Line

A/B Testing

Let’s say that you work at TikTok. The goal for the company next quarter is to increase the daily active users metric (DAU).

<ul>
<li>Executive A believes the best way to increase DAU is to improve the recommendation algorithm in TikTok’s “For You” feature (Tiktok news feed).</li>

<li>Executive B believes the best way to increase DAU is to acquire more new users.</li>

<li>Executive C believes the best way to increase DAU is to improve the creator tools.</li>
</ul>

The engineering team must prioritize one feature at a time.

<ol>
<li>How do you figure out which executive is right?</li>

<li>What data points and metrics would help validate your choice?</li>
</ol>

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

Cigna Interview Questions

Cigna Interview Guides

Cigna Interview Questions

Challenge

Cigna Salaries by Position

Discussion & Interview Experiences

Discussion & Interview Experiences