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RTI International Interview Questions

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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 would you prepare and monitor a churn model before and after production deployment?

Model Deployment Pipeline

Making data-driven insights actionable for those without technical expertise

Simple Explanations

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

Your Strengths and Weaknesses

Product Sense & Metrics

How would you measure the success of Linkedin’s newsfeed ranking algorithm and approach conflicting success metrics

Ranking Metrics

The role of A/B testing in measuring the success rate of an analytics experiment

Success Measurement

Vision Setting and Execution Strategy

Machine Learning

What metrics would you use to track accuracy and validity of the model

Spam Classifier 

Write a query to create a pivot table that shows total sales for each branch by year

Branch Sales Pivot

Analytics

Describing a real-world data cleaning and organization project

Data Cleaning Experiences

<p>You’re a data scientist at YouTube. Leadership wants to revisit how the platform measures success, not for a specific new feature, but for the product as a whole given its current state.</p>

<p>How would you define and measure success for YouTube? Identify your primary and secondary metrics, explain your rationale for each, and discuss how you would choose the right thresholds for any engagement-based metrics you propose.</p>


<p>Let’s say you work as a data scientist on the engagement team.</p>

<ol>
<li><p>How would you measure the success of Linkedin’s newsfeed ranking algorithm?</p></li>

<li><p>Let’s say some of the success metrics that you are tracking for the newsfeed algorithm are going up while others are going down. How would you approach this?</p></li>
</ol>


<p>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?</p>


<p>Let’s say you are tasked with building a spam classifier for emails. Assume that you’ve built a V1 of the model.</p>

<p>What metrics would you use to track accuracy and validity of the model?</p>


<p>Let’s say you’ve just finished building a churn prediction model for a subscription service, and the team is considering deploying it to automatically trigger retention emails for users at risk of leaving.</p>

<p>How would you ensure this model is ready for production, and what steps would you take to monitor its performance after launch?</p>


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


<p>Describe a time when you had to define a long-term vision for a project or team and move it from concept to reality.</p>

<p>Interviews for leadership or senior technical roles look for more than just “finishing a project.” Your answer should specifically address:</p>

<ol>
<li><strong>The “Why”</strong>: What data or organizational gap necessitated this vision?</li>
<li><strong>The Framework</strong>: How did you translate a high-level goal into a roadmap?</li>
<li><strong>The Friction</strong>: How did you handle stakeholders or team members who were skeptical of the new direction?</li>
</ol>


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


<p>Your company, a multinational retail corporation, has been storing sales data from various branches worldwide in separate tables according to the year the sales were made. The current data structure is proving inefficient for business analytics and the management has requested your expertise to streamline the data.</p>

<p>Write a query to create a pivot table that shows total sales for each branch by year.</p>

<p><em>Note: Assume that the sales are represented by the <code>total_sales</code> column and are in USD. Each branch is represented by its unique <code>branch_id</code></em>.</p>

<h3><strong>Example:</strong></h3>

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

<p>For simplicity, consider two years: 2021 and 2022.</p>

<p><code>sales_2021</code> table</p>

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

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

<tr>
<td>branch_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>total_sales</td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><code>sales_2022</code> table</p>

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

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

<tr>
<td>branch_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>total_sales</td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

<p><code>sales_pivot</code> table</p>

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

<tbody>
<tr>
<td>branch_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>total_sales_2021</td>
<td>INTEGER</td>
</tr>

<tr>
<td>total_sales_2022</td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p>This output pivot table shows the total sales for each branch, broken down by year.</p>


<p>When an interviewer asks a question along the lines of:</p>

<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>

<p>How would you respond?</p>


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

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

Your Strengths and Weaknesses I

<p>Which of the following is an acceptable strategy when discussing weaknesses in an interview?</p>

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

Your Strengths and Weaknesses II

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


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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