Prepare for and practice interview questions from Glatfelter across topics like Statistics, SQL, Machine Learning and more.

Glatfelter Interview Questions

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Machine Learning

Which model would perform better and why?

Booking Regression

Lasso vs Ridge

Find the linear regression parameters of a given matrix

Linear Regression Parameters

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

Statistics

What are the assumptions of linear regression?

Assumptions of Linear Regression

How do you detect and handle correlation between variables in linear regression, and what happens if you ignore it

Correlation in Regression

What are the regression coefficients when regressing Y on X versus X on Y, given Y = X plus normal white noise?

Regress Y on X

Product Sense & Metrics

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

Success Measurement

Vision Setting and Execution Strategy

Analytics

Describing a real-world data cleaning and organization project

Data Cleaning Experiences

Let’s say we want to build a model to predict booking prices on Airbnb.

Between linear regression and random forest regression, which model would perform better and why?

What are the two key concepts of random forest regression?

What are the two key concepts of random forest regression?

What’s the difference between Lasso and Ridge Regression?

Under what circumstances is it recommended to use Lasso regression over Ridge regression?

Under what circumstances is it recommended to use Lasso regression over Ridge regression?

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?

What are the assumptions of linear regression?

Which assumption of the residuals of the standard linear regression model can not be overcome by increasing the sample size?

Regression assumptions

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.

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.

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

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

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>

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

Given a matrix of X and y values, write a function to generate a transposed matrix and estimate the parameters for linear regression.

Example:

Input:

<pre tabindex="0" class="chroma"><code>A = [[1, 5], [4,8], [5,9]]

</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>#Output
linear_regression(A) -&gt; [[[1, 4, 5], [5, 8, 9]] , [4, 1]]
</code></pre>

Note: Return the solutions a list of `[[A_T] , [coefficients]]

How do you detect and handle correlation between variables in linear regression? What will happen if you ignore the correlation in the regression model?

Suppose we have two variables, X and Y, where Y = X + some normal white noise.

<ol>
<li>What will our coefficient be if we run a regression of Y on X?</li>

<li>What happens if we run a regression of X on Y?</li>
</ol>

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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