Prepare for and practice interview questions from Travel + Leisure Co. across topics like SQL, Statistics, Machine Learning and more.

Travel + Leisure Co. Interview Questions

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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

Machine Learning

Missing Housing Data

Food Prep Features

Brainteasers

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

Data Pipelines

Describing a real-world data cleaning and organization project

Data Cleaning Experiences

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

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

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

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

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

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

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

Vision Setting and Execution Strategy

Business Case

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?



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.

How would you determine which search engine performed better? Which metrics would you track?

Comparing Search Engines

A/B Testing

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. 

What kind of model would we build and what features would we use?

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.

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

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

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?



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

Travel + Leisure Co. Interview Questions

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