Prepare for and practice interview questions from Calico Life Sciences across topics like Algorithms, Takehome, Machine Learning and more.

Calico Life Sciences Interview Questions

Calico Life Sciences Interview Guides

Machine Learning

RMS Error

Missing Housing Data

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

Softmax vs Logistic

How to model merchant acquisition in a new market?

Merchant Acquisition

Random Forest Explanation

Data Structures & Algorithms

Moving Window

Given a json string with nested objects, write a function that flattens all the objects to a single key-value dictionary.

Flatten JSON

Write a function to impute the median price of the selected California cheeses in place of the missing values.

Impute Median

Given a list of strings, write a function that returns the longest common prefix

Common Prefix

Valid Anagram

Select the 2nd highest salary in the engineering department

2nd Highest Salary

Last Transaction

Cumulative Distribution

Given a users table, write a query to get the cumulative number of new users added by the day, with the total reset every month. 

Cumulative Reset

Find the second longest flight between each pair of cities.

Second Longest Flight

Behavioral

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

Why Do You Want to Work With Us

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

Statistics

P-value to a Layman

Reducing Error Margin

Unbiased Estimator

Density to Cumulative

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

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

Write a SQL query to select the 2nd highest salary in the engineering department.

Note: If more than one person shares the highest salary, the query should select the next highest salary.

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

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

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

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

Given a table of bank transactions with columns <code>id</code>, <code>transaction_value</code>, and <code>created_at</code> representing the date and time for each transaction, write a query to get the last transaction for each day.

The output should include the id of the transaction, datetime of the transaction, and the transaction amount. Order the transactions by datetime.

Example:

Input:

<code>bank_transactions</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>created_at</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>transaction_value</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>
Output:

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

<tbody>
<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>transaction_value</code></td>
<td>FLOAT</td>
</tr>

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

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

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

Explain the difference between the XGBoost and random forest algorithms and give an example where you would use one over the other.

Why might a data scientist choose to use XGBoost instead of Random Forest for a particular machine learning task?

Why might a data scientist choose to use XGBoost instead of Random Forest for a particular machine learning task?

XGBoost vs Random Forest: Choice

Xgboost vs Random Forest

Given the two tables, write a SQL query that creates a cumulative distribution of the number of comments per user. Assume bin buckets class intervals of one.

Example:

Input:

<code>users</code> table

<table>
<thead>
<tr>
<th>Columns</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>

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>neighborhood_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>sex</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<code>comments</code> table

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

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

<tr>
<td><code>body</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
Output:

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

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

<tr>
<td><code>cum_total</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>

Given a <code>users</code> table, write a query to get the cumulative number of new users added by the day, with the total reset every month. 

Example:

Input:

<code>users</code> table

<table>
<thead>
<tr>
<th>Columns</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>

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>DATE</th>
<th>INTEGER</th>
</tr>
</thead>

<tbody>
<tr>
<td>2020-01-01</td>
<td>5</td>
</tr>

<tr>
<td>2020-01-02</td>
<td>12</td>
</tr>

<tr>
<td>…</td>
<td>…</td>
</tr>

<tr>
<td>2020-02-01</td>
<td>8</td>
</tr>

<tr>
<td>2020-02-02</td>
<td>17</td>
</tr>

<tr>
<td>2020-02-03</td>
<td>23</td>
</tr>
</tbody>
</table>

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

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

Let’s say we want to launch a re-design of a landing page to improve the click-through rate. We can do this by implementing an AB test.
Given that we launch an AB test, how would you infer if the results of the click-through rate were statistically significant or not?

How can you accurately conclude if the results of an A/B test, conducted to evaluate the effectiveness of a landing page redesign, are statistically significant?

How can you accurately conclude if the results of an A/B test, conducted to evaluate the effectiveness of a landing page redesign, are statistically significant?

Statistically Significant Test

Precisely ascertain whether the outcomes of an A/B test, executed to assess the impact of a landing page redesign, exhibit statistical significance.

A/B Testing

We want to build a model to predict housing prices in the city of Seattle. We’ve scraped 100K sold listings over the past three years but found that around 20% of the listings are missing square footage data.

How do we deal with the missing data to construct our model?

We want to build a model to predict housing prices in the city of Seattle. We’ve scraped 100K sold listings over the past three years, but have discovered that around 20% of the listings are missing square footage data.

How would you approach dealing with this missing data, in order to construct the most useful predictive model possible?

We want to build a model to predict housing prices in the city of Seattle. We’ve scraped 100K sold listings over the past three years, but have discovered that around 20% of the listings are missing square footage data.

How would you approach dealing with this missing data, in order to construct the most useful predictive model possible?



Tell me about a project in which you had to clean and organize a large dataset.

Describing a real-world data cleaning and organization project

Data Cleaning Experiences

Analytics

Regularization and cross-validation are two common techniques used to improve the performance of machine learning algorithms.

When should you use one versus the other?

Given the choices below, which one is an example of a scenario where we should be using regularization?

Given the choices below, which one is an example of a scenario where we should be using regularization?

Regularization Example

Regularization and Validation

Given a table of <code>subscriptions</code>, write a query to get the retention rate of each monthly cohort for each <code>plan_id</code> for the three months after sign-up.

Order your output by <code>start_month</code>, <code>plan_id</code>, then <code>num_month</code>.

If an <code>end_date</code> is in the same month as <code>start_date</code> we say the subscription was not retained in the first month.

If the <code>end_date</code> occurs in the month after the month of <code>start_date</code>, the subscription was not retained in the second month. And so on for the third.

The <code>end_date</code> field is <code>NULL</code> if the user has not canceled.

<h3>Schema:</h3>

Input:

<code>subscriptions</code> table

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

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

<tr>
<td><code>start_date</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>end_date</code></td>
<td>DATETIME</td>
</tr>

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

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

<tbody>
<tr>
<td><code>start_month</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>num_month</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>plan_id</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>retained</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>

<h3>Example</h3>

Input:

<code>subscriptions</code> table

<table>
<thead>
<tr>
<th>user_id</th>
<th>start_date</th>
<th>end_date</th>
<th>plan_id</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>2020-01-10 00:00:00</td>
<td>2020-03-31 23:59:59</td>
<td>plan1</td>
</tr>

<tr>
<td>2</td>
<td>2020-01-10 00:00:00</td>
<td>2020-02-01 23:59:59</td>
<td>plan1</td>
</tr>

<tr>
<td>3</td>
<td>2020-01-10 00:00:00</td>
<td>2020-07-31 23:59:59</td>
<td>plan1</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>start_month</th>
<th>num_month</th>
<th>plan_id</th>
<th>retained</th>
</tr>
</thead>

<tbody>
<tr>
<td>2020-01-01</td>
<td>1</td>
<td>plan1</td>
<td>1.0</td>
</tr>

<tr>
<td>2020-01-01</td>
<td>2</td>
<td>plan1</td>
<td>0.67</td>
</tr>

<tr>
<td>2020-01-01</td>
<td>3</td>
<td>plan1</td>
<td>0.33</td>
</tr>
</tbody>
</table>
Explanation:

<ul>
<li>Cohort: All subscriptions starting in January 2020.</li>
<li>Month 1 retention: All 3 users were active in the first month → 3⁄3 = 1.0</li>
<li>Month 2 retention: Only users 1 and 3 were active in Feb → 2⁄3 ≈ 0.67</li>
<li>Month 3 retention: Only user 3 was active in Mar → 1⁄3 ≈ 0.33</li>
</ul>

Subscription Retention

Let’s say we have an <code>annual_payments</code> table. Answer the following three questions via SQL queries and output them as a table with the answers to each question.

<ol>
<li>How many total transactions are in this table?</li>
<li>How many different users made transactions?</li>
<li>How many transactions listed as <code>&#34;paid&#34;</code> have an amount greater or equal to 100?</li>
<li>Which product made the highest revenue? (use only transactions with a <code>&#34;paid&#34;</code> status)</li>
</ol>

Example:

Input:

<code>annual_payments</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>amount</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>status</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>user_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>amount_refunded</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>product</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>last_updated</code></td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
Output:

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

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

<tr>
<td><code>answer</code></td>
<td>TEXT</td>
</tr>
</tbody>
</table>

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

Count Transactions

Given a table of <code>flights</code>, extract the 2nd flight with the most duration between each pair of cities. Order the flights by the flight <code>id</code> ascending.

Note: For any cities <code>X</code> and <code>Y</code>, <code>(source_location=X, destination_location=Y)</code> and <code>(source_location=Y, destination_location=X)</code> are counted as the same pair of cities.

Note: If there are fewer than two flights between two cities, there is no 2nd longest flight.

Example:

Input:

<code>flights</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>destination_location</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>source_location</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>plane_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>flight_start</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>flight_end</code></td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
Output:

<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>destination_location</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>source_location</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>flight_start</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>flight_end</code></td>
<td>DATETIME</td>
</tr>
</tbody>
</table>

What’s the relationship between PCA and K-means clustering?

What does the variable “k” in k-means clustering refer to?

What does the variable "k" in k-means clustering refer to?

Input of K-means

PCA and K-Means

Write a function to get a sample from a standard normal distribution.

Write a function to get a sample from a standard normal distribution.

Normal Distribution Sample

Write a function <code>calculate_rmse</code> to calculate the root mean squared error of a regression model. The function should take in two lists, one that represents the predictions <code>y_pred</code> and another with the target values <code>y_true</code>.

Input:

Example 1: 

<pre tabindex="0" class="chroma"><code>y_pred = [3,4,5]
y_true = [3,4,5]

def calculate_rmse(y_pred,y_true) -&gt; 0
</code></pre>

Example 2:

<pre tabindex="0" class="chroma"><code>y_pred = [3,4,5]
y_true = [5,4,3]

def calculate_rmse(y_pred,y_true) -&gt; 1.63
</code></pre>

Let’s say we have a sample size of \(n\). The margin of error for our sample size is 3.

How many more samples would we need to decrease the margin of error to 0.3?

Let's say we have a sample size of N. The margin of error for our sample size is 3.

How many more samples would we need to decrease the margin of error to 0.3?

Let's say we have a sample size of N. The margin of error for our sample size is 3.

How many more samples would we need to decrease the margin of error to 0.3?

Given a list of numbers <code>nums</code> and an integer <code>window_size</code>, write a function <code>moving_window</code> to find the moving window average.

Example 1:

Input:

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

Output:

<pre tabindex="0" class="chroma"><code>def moving_window(input, window_size) -&gt; [1, 1.5, 2, 3, 4, 5]
</code></pre>

Example 2:

Input:

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

Output:

<pre tabindex="0" class="chroma"><code>def moving_window(input, window_size) -&gt; [1, 1.5, 2, 2.5, 3.5, 4.5]
</code></pre>

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

How would you convey insights and the methods you use to a non-technical audience?

Making data-driven insights actionable for those without technical expertise

Simple Explanations

Given two strings, write a function to return <code>True</code> if the strings are anagrams of each other and <code>False</code> if they are not.

Note: A word is not an anagram of itself.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>string_1 = &#34;listen&#34;
string_2 = &#34;silent&#34;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>True
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>string_1 = &#34;banana&#34;
string_2 = &#34;bandana&#34;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>False
</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

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