The Oportun Business Intelligence interview guide, interview questions, salary data, and interview experiences.

Oportun Business Intelligence Interview Guide

<p>Getting ready for an Business Intelligence interview at Oportun? The Oportun Business Intelligence interview span across 10 to 12 different question topics. In preparing for the interview:</p>
<ul>
<li>Know what skills are necessary for Oportun Business Intelligence roles.</li>
<li>Gain insights into the Business Intelligence interview process at Oportun.</li>
<li>Practice real Oportun Business Intelligence interview questions.</li>
</ul>
<p>Interview Query regularly analyzes interview experience data, and we've used that data to produce this guide, with sample interview questions and an overview of the Oportun Business Intelligence interview.</p>


Getting ready for an Business Intelligence interview at Oportun? The Oportun Business Intelligence interview span across 10 to 12 different question topics. In preparing for the interview:

- Know what skills are necessary for Oportun Business Intelligence roles.
- Gain insights into the Business Intelligence interview process at Oportun.
- Practice real Oportun Business Intelligence interview questions.

Interview Query regularly analyzes interview experience data, and we've used that data to produce this guide, with sample interview questions and an overview of the Oportun Business Intelligence interview.
        

Overview

Oportun Business Intelligence Salary

<p>We've gathered this data from parsing thousands of interview experiences sourced from members.</p>

We've gathered this data from parsing thousands of interview experiences sourced from members.

Oportun Business Intelligence Interview Process

<p>We’re given a table of bank transactions with three columns, <code>user_id</code>, a deposit or withdrawal value (determined if the value is positive or negative), and <code>created_at</code> time for each transaction.</p>

<p>Write a query to get the total three-day rolling average for deposits by day.</p>

<p><em>Note: Please use the format <code>&#39;%Y-%m-%d&#39;</code> for the date in the outout</em></p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<p><code>bank_transactions</code> table</p>

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

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

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

<tbody>
<tr>
<td><code>dt</code></td>
<td>VARCHAR</td>
</tr>

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


Rolling Bank Transactions

<p>Let’s say you’re working for a social media company trying to grow your user base. You know that people tend to use the same social network as their peers, so you want to target all your growth and marketing efforts on specific age groups so as to take advantage of network effects.</p>

<p>You wish to begin with the age group that is currently generating the most connections (fastest growing).</p>

<p>Write a SQL query to find the average number of accepted friend requests for each age group and order the results in descending order.</p>

<p><em>Note: The average number of accepted friend requests is calculated as the total number of friend requests sent by an age group that was accepted divided by the number of members in said age group.</em></p>

<p><em>We can assume the tables hold valid data in that there are no duplicate friend requests, friend requests are only accepted once, and user A can only accept a friend request from User B after B sends A a friend request.</em></p>

<p><em>The result should be rounded to two decimal places.</em></p>

<h3>Example:</h3>

<p><strong>Input:</strong></p>

<p><strong><code>friend_requests</code></strong></p>

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

<tbody>
<tr>
<td>requester_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>receiver_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>sent_at</td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
<p><strong><code>requests_accepted</code></strong></p>

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

<tbody>
<tr>
<td>accepter_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>requester_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>accepted_at</td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
<p><strong><code>age_groups</code></strong></p>

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

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

<tr>
<td>age_group</td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

<tbody>
<tr>
<td>age_group</td>
<td>VARCHAR</td>
</tr>

<tr>
<td>average_acceptance</td>
<td>FLOAT</td>
</tr>
</tbody>
</table>


Find the average number of accepted friend requests for each age group that sent the requests.

Avg Friend Requests By Age Group

<p>What are the logistic and softmax functions? What is the difference between the two?</p>

<p>What makes them useful for use in logistic regression?</p>


<p>In logistic regression, how is the predicted class determined based on the logistic function?</p>


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

Softmax vs Logistic II

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


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

Softmax vs Logistic I

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

Softmax vs Logistic

Oportun Business Intelligence Interview Questions

<p><b>Senior Product Manager, Pathward Originations</b></p><ul><li>They are seeking a Senior Product Manager for Loan Originations, responsible for delivering critical capabilities to improve member experience together with their bank partner, Pathward.</li></ul><p><b>Company Information</b></p><p>Oportun is an A.I.-powered digital banking platform that seeks to make financial health effortless for anyone. Driven by a mission to provide inclusive and affordable financial services, Oportun helps millions of hardworking members meet their daily borrowing, savings, banking, and investing needs.</p><p><b>Company Specialties:</b><br/><i>Financial Services, Responsible Lending, Pre-Paid Debit Cards, Serving the Underbanked, Data Analytics, FinTech, Personal Loans, Auto Loans, and Machine Learning.</i></p>
#J-18808-Ljbffr

Senior Product Manager, Pathward Originations
They are seeking a Senior Product Manager for Loan Originations, responsible for delivering critical capabilities to improve member experience together with their bank partner, Pathward.
Company Information
Oportun is an A.I.-powered digital banking platform that seeks to make financial health effortless for anyone. Driven by a mission to provide inclusive and affordable financial services, Oportun helps millions of hardworking members meet their daily borrowing, savings, banking, and investing needs.
Company Specialties:
Financial Services, Responsible Lending, Pre-Paid Debit Cards, Serving the Underbanked, Data Analytics, FinTech, Personal Loans, Auto Loans, and Machine Learning.

#J-18808-Ljbffr

Oportun Business Intelligence Interview Guide

Overview

Oportun Business Intelligence Salary

Oportun Business Intelligence Interview Process

Oportun Business Intelligence Interview Questions

Oportun Business Intelligence Jobs