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Machine Learning

Missing Housing Data

85% vs 82%

Food Prep Features

Classification and Regression

Matching Siblings

Business Case

Declining Applicants

Game Feature Home

How to boost presence in high-demand city areas

Incentive Scheme

Meta in an Emerging Market

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

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

Your Strengths and Weaknesses

Analytics

How would you determine customer service quality through a chat box?

Measuring Customer Service Quality

Describing a real-world data cleaning and organization project

Data Cleaning Experiences

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

International e-Commerce Warehouse

Find top frequent flights between source and destination.

Flight Routes - 2

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


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


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


<p>What does a p-value in a statistical test represent?</p>

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

P-value to a Layman I

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

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

P-value to a Layman

Statistics

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


Vision Setting and Execution Strategy

Product Sense & Metrics

<p>What are the key differences between classification models and regression models?</p>


<p>What is the MOST important difference between regression and classification models?</p>


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

Classification vs Regression

<p>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.</p>

<p>How do we deal with the missing data to construct our model?</p>


<p>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.</p>

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


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?



<p>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.</p>

<p>How would you go about deciding whether Google should build it?</p>


<p>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.</p>

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

<p>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?</p>

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


<p>Let’s say that you work on the revenue forecasting team at a company like Facebook.</p>

<p>An executive comes to you asking about how much revenue Facebook will make in the coming year.</p>

<p>How would you forecast revenue for the next year?</p>


Forecasting New Year Revenue

Forecasting & Time Series

<p>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.</p>


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

Risk Assessment Model

<p>Let’s say you work at a food delivery company.</p>

<p>How would you measure the effectiveness of giving extra pay to delivery drivers during peak hours to meet the demand from consumers?</p>


Extra Delivery Pay

A/B Testing

<p>Let’s say that you’re looking at the metrics of a job board. You see that the number of job postings per day has more or less remained the same the last few months but the number of applicants that have applied to jobs has steadily been decreasing.</p>

<p>Why would this be happening?</p>


<p>Let’s say that you’re working on building a model to detect potential bombs at a border crossing.</p>

<p>How would you design the model’s inputs and outputs, measure its accuracy, and test your model?</p>


<p>Let’s say you are building a neutral net to detect if a nuclear missile is incoming.</p>

<p>What metric would be most important to look at?</p>


Let's say you are building a neutral net to detect if a nuclear missile is incoming.

What metric would be most important to look at?

Nuclear Missile Detection

Bomb Detection

<p>Let’s say you’re working on a building a better search engine for Google. You build it and want to see if it serves better results than the existing one in production.</p>

<p>How would you determine which search engine performed better? Which metrics would you track?</p>


Comparing Search Engines

<p>Let’s say that you work at <strong>Facebook.</strong> Your team focuses on helping small businesses increase sales through the Marketplace app on Facebook.</p>

<p>How would you determine the customer service quality through the chat box for all interactions involving small businesses selling items to consumers?</p>


<p>One day you are invited to your kid’s kindergarten. The children there ask about your work as a machine learning engineer.</p>

<p>How would you explain neural networks to the kids there?</p>


Explain Neural Nets to Kids

<p>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.</p>

<p>How would you design this system?</p>


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

Distributed Authentication Model

Concurrent & Distributed Systems

<p>Let’s say we want to build a model to predict the time spent for a restaurant to prepare food from the moment an order comes in until the order is ready. </p>

<p>What kind of model would we build and what features would we use?</p>


<p>Let’s say you’re working on DoorDash demand side deliveries. DoorDash is launching delivery services in New York City and Charlotte and needs a process for selecting dashers (delivery drivers).</p>

<p>Let’s say we want to create a model to determine which Dashers should deliver a particular order.</p>

<p>We currently have these features already in our model.</p>

<ul>
<li>Dasher distance to the restaurant</li>
<li>Number of Dasher reviews from customers</li>
<li>Number of Dasher successful deliveries</li>
</ul>

<p>Which feature would be the best additional input to our existing features to improve the model?</p>


Let's say you're working on DoorDash demand side deliveries. DoorDash is launching delivery services in New York City and Charlotte and needs a process for selecting dashers (delivery drivers).

Let's say we want to create a model to determine which Dashers should deliver a particular order.

We currently have these features already in our model. 

- Dasher distance to the restaurant
- Number of Dasher reviews from customers
- Number of Dasher successful deliveries

Which feature would be the best additional input to our existing features to improve the model?



Dasher Model

<p>Let’s say you work at Meta (Facebook).</p>

<p>The company is looking to grow its userbase in an emerging market.</p>

<p>How would you approach promoting Meta’s products?</p>


<p>Let’s say that you’re working on the Uber app. </p>

<p>How would you design an incentive scheme for drivers such that they would more likely go into city areas where demand is high?</p>


<p>What do you think the distribution of time spent per day on Facebook looks like? What metrics would you use to describe that distribution?</p>


Time on FB Distribution

<p>We have two models: one with 85% accuracy, one 82%. Which one do you pick?</p>


<p>Let’s say that you’re a data scientist working for Facebook.</p>

<p>A product manager has asked you to develop a method to match users to their siblings on Facebook.</p>

<ol>
<li><p>How would you evaluate a method or algorithm to match users with their siblings?</p></li>

<li><p>What metrics might you use?</p></li>
</ol>


<p>Let’s say you’re given 90 days of ride data.</p>

<p>How would you use the ride data to project the lifetime of a new driver on the system? What about the lifetime value of the driver?</p>


How would you use the ride data to project the lifetime of a new driver on the system?

Lifetime Driver

<p>Delta Airlines is analyzing how passengers travel across its domestic network. The analytics team wants to understand which routes are most commonly flown out of each airport, and how long these popular journeys usually take. You’re working as a data analyst at Delta Airlines, and you’re provided with a table <code>flights</code>  where each row represents one scheduled flight.</p>

<p>Write a SQL query to return the top 5 most frequent flight routes for each origin, ordered first by frequency descending, and then (for ties) by average journey duration ascending.</p>

<p><em>Notes:</em></p>

<ol>
<li><em>A route is defined as a unique <code>(origin, destination)</code> pair.</em></li>
<li><em>Assume there is no more then 5 routes with the same frequency and average filght time.</em></li>
</ol>

<h2>Schema:</h2>

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

<h3><code>flights</code></h3>

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

<tbody>
<tr>
<td>flight_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>origin</td>
<td>VARCHAR</td>
</tr>

<tr>
<td>destination</td>
<td>VARCHAR</td>
</tr>

<tr>
<td>departure_time</td>
<td>DATETIME</td>
</tr>

<tr>
<td>arrival_time</td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
<p>Output:</p>

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

<tbody>
<tr>
<td>origin</td>
<td>VARCHAR</td>
</tr>

<tr>
<td>destination</td>
<td>VARCHAR</td>
</tr>

<tr>
<td>route_frequency</td>
<td>INTEGER</td>
</tr>

<tr>
<td>avg_duration_minutes</td>
<td>DATETIME</td>
</tr>
</tbody>
</table>

<h2>Example:</h2>

<h3><strong>Input Data</strong></h3>

<h3><code>flights</code></h3>

<table>
<thead>
<tr>
<th>flight_id</th>
<th>origin</th>
<th>destination</th>
<th>departure_time</th>
<th>arrival_time</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>JFK</td>
<td>LAX</td>
<td>2025-11-19 08:00</td>
<td>2025-11-19 11:30</td>
</tr>

<tr>
<td>2</td>
<td>JFK</td>
<td>LAX</td>
<td>2025-11-19 14:00</td>
<td>2025-11-19 17:40</td>
</tr>

<tr>
<td>3</td>
<td>JFK</td>
<td>ORD</td>
<td>2025-11-19 09:00</td>
<td>2025-11-19 10:45</td>
</tr>

<tr>
<td>4</td>
<td>JFK</td>
<td>ORD</td>
<td>2025-11-20 09:00</td>
<td>2025-11-20 10:42</td>
</tr>

<tr>
<td>5</td>
<td>JFK</td>
<td>MIA</td>
<td>2025-11-19 12:00</td>
<td>2025-11-19 15:00</td>
</tr>

<tr>
<td>6</td>
<td>JFK</td>
<td>MIA</td>
<td>2025-11-20 12:00</td>
<td>2025-11-20 14:55</td>
</tr>

<tr>
<td>7</td>
<td>LAX</td>
<td>SFO</td>
<td>2025-11-19 07:00</td>
<td>2025-11-19 08:25</td>
</tr>

<tr>
<td>8</td>
<td>LAX</td>
<td>SFO</td>
<td>2025-11-19 16:00</td>
<td>2025-11-19 17:20</td>
</tr>

<tr>
<td>9</td>
<td>LAX</td>
<td>SEA</td>
<td>2025-11-19 06:00</td>
<td>2025-11-19 08:40</td>
</tr>

<tr>
<td>10</td>
<td>LAX</td>
<td>SEA</td>
<td>2025-11-20 06:00</td>
<td>2025-11-20 08:35</td>
</tr>
</tbody>
</table>

<h2><strong>Output</strong></h2>

<table>
<thead>
<tr>
<th>origin</th>
<th>destination</th>
<th>route_frequency</th>
<th>avg_duration_minutes</th>
</tr>
</thead>

<tbody>
<tr>
<td>JFK</td>
<td>LAX</td>
<td>2</td>
<td>210</td>
</tr>

<tr>
<td>JFK</td>
<td>ORD</td>
<td>2</td>
<td>103</td>
</tr>

<tr>
<td>JFK</td>
<td>MIA</td>
<td>2</td>
<td>172</td>
</tr>

<tr>
<td>LAX</td>
<td>SFO</td>
<td>2</td>
<td>82</td>
</tr>

<tr>
<td>LAX</td>
<td>SEA</td>
<td>2</td>
<td>157</td>
</tr>
</tbody>
</table>
<p><em>Explanation:</em></p>

<p>From each origin (JFK, LAX), the query returns the top 5 most frequent destination routes. Since each origin has only two routes in this dataset, they are all included. For routes with the same frequency, sorting is done by increasing average duration.</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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