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Stand Together Interview Questions

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A/B Testing

How would you assess the validity of the result?

Experiment Validity

How would you determine if the new model predicts delivery times better than the old model

Delivery Estimate Model

Button AB Test

A/B Test Power Size

How would you make a control group and test group to account for network effects

Network Experiment Design

Machine Learning

Which model would perform better and why?

Booking Regression

Lasso vs Ridge

In which case would you use a bagging algorithm versus a boosting algorithm

Bagging vs Boosting

Explain the key differences between classification models and regression models

Classification and Regression

When should you use regularization versus cross-validation

Regularization and Validation

Statistics

Say you are given a dataset of perfectly linearly separable data. What would happen when you run logistic regression?

Perfectly Separable

What are the assumptions of linear regression?

Assumptions of Linear Regression

Explain what an unbiased estimator is and provide a layman-friendly example

Unbiased Estimator

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

Correlation in Regression

Behavioral

How would you answer when an Interviewer asks why you applied to their company?

Why Do You Want to Work With Us

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

Your Strengths and Weaknesses

Forecasting & Time Series

Explain what time series models are and why they’re needed instead of simpler regression models

Why Do We Need Time Series Models?

Let’s say that your company is running a standard control and variant AB test on a feature to increase conversion rates on the landing page. The PM checks the results and finds a .04 p-value.

How would you assess the validity of the result?

A team wants to A/B test multiple different changes through a sign-up funnel.

For example, on a page, a button is currently red and at the top of the page. They want to see if changing a button from red to blue and/or from the top of the page to the bottom of the page will increase click-through.

How would you set up this test?

Your team wants to run an AB test on the color (red or blue) and position (top/bottom of the page) of a button that links to a promotion.

How many different variants of the test should you run to see the effect that a red button has on the bottom of the page, if currently the button is blue and on the top of the page.

Your team wants to run an AB test on the color (red or blue) and position (top/bottom of the page) of a button that links to a promotion.

How many different variants of the test should you run to see the effect that a red button has on the bottom of the page, if currently the button is blue and on the top of the page.

A/B Test Variants

Let’s say we’re comparing two machine learning algorithms. In which case would you use a bagging algorithm versus a boosting algorithm? 

Give an example of the tradeoffs between the two.

In machine learning, when would you use a bagging algorithm over a boosting algorithm?

In machine learning, when would you use a bagging algorithm over a boosting algorithm?

Bagging vs. Boosting

In an A/B test, how can you check if assignment to the various buckets was truly random?

In an A/B test, which of the following is a common method to determine if the assignment to the test groups was truly random?

In an A/B test, which of the following is a common method to determine if the assignment to the test groups was truly random?

Random Bucketing

How can you check if assignment to the various buckets was truly random

Let’s say you want to test the close friends feature on Instagram Stories.

How would you make a control group and test group to account for network effects?

What could be a potential risk when Facebook segments the metrics of the user interface change by market or demographic groups?

What could be a potential risk when Facebook segments the metrics of the user interface change by market or demographic groups?

Network Experiment Design I

What is the primary metric that Facebook should monitor if it decides to make the user interface of its posting feature more like Instagram's?

What is the primary metric that Facebook should monitor if it decides to make the user interface of its posting feature more like Instagram's?

Network Experiment Design II

If Facebook changes its user interface to mimic Instagram's, which adverse effect should they anticipate and monitor?

If Facebook changes its user interface to mimic Instagram's, which adverse effect should they anticipate and monitor?

Network Experiment Design III

Let’s say we want to build a new delivery time estimate model for consumers ordering food delivery.

How would you determine if the new model predicts delivery times better than the old model?

When rolling out a new delivery time estimate model in production, what is an important consideration?

When rolling out a new delivery time estimate model in production, what is an important consideration?

Delivery Estimate Model I

What is one way to measure the performance of a new delivery time estimate model for food delivery?

What is one way to measure the performance of a new delivery time estimate model for food delivery?

Delivery Estimate Model II

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?

Say you are given a dataset of perfectly linearly separable data.

What would happen when you run logistic regression?

What happens when logistic regression is run on a dataset of perfectly linearly separable data?

What happens when logistic regression is run on a dataset of perfectly linearly separable data?

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?

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

Let’s say that you work at a B2B SAAS company that’s interested in testing the pricing of different levels of subscriptions.

Your project manager comes to you and asks you to run a two-week-long A/B test to test an increase in pricing.

How would you approach designing this test? How would you determine whether the increase in pricing is a good business decision?

How would you design a two-week A/B test to determine whether a pricing increase is a good business decision

Testing Price Increase

Let’s say you have to analyze the results of an AB test.

One variant of the AB test has a sample size of 50K users and the other has a sample size of 200K users.

Given the unbalanced size between the two groups, can you determine if the test will result in bias towards the smaller group?

Given an AB test where one variant has a sample size of 50K users and the other has a sample size of 200K users, what is the likely effect of the unbalanced sizes on the test results?

Given an AB test where one variant has a sample size of 50K users and the other has a sample size of 200K users, what is the likely effect of the unbalanced sizes on the test results?

Sample Size Bias

Can you determine if the test will result in bias towards the smaller group?

Let’s say we’re testing a new UI with the goal to increase conversion rates. We test it by giving the new UI to a random subset of users.

The test variant wins by 5% on the target metric. What would you expect to happen after the new UI is applied to all users? Will the metric actually go up by ~5%, more, or less?

Note: Assume there is no novelty effect.

What would you expect to happen after the new UI is applied to all users?

New UI Effect

Uber Rider deals with huge amounts of data but at Uber Fleet, the amount of data for experimentation is rather low. Let’s say you run an AB test for Uber Fleet and find out that the distribution is not normal.

What kind of analysis would you run and how would you measure which variant won?

Which of the following is NOT an acceptable way to deal with AB testing on a non-normally distributed population?

Which of the following is NOT an acceptable way to deal with AB testing on a non-normally distributed population?

Non-Normal AB Test

What kind of analysis would you run and how would you measure which variant won

Non-Normal AB Testing

What are the key differences between classification models and regression models?

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

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

Classification vs Regression

What is an unbiased estimator and can you provide an example for a layman to understand?

What makes an estimator of a population statistic unbiased?

What are time series models? Why do we need them when we have less complicated regression models?

What can happen if a traditional regression model is used on data that has autocorrelation?

What can happen if a traditional regression model is used on data that has autocorrelation?

Why Do We Need Time Series Models? I

Why can't standard regression models be used effectively for data points where current values are influenced by past values?

Why can't standard regression models be used effectively for data points where current values are influenced by past values?

Why Do We Need Time Series Models? II

When an interviewer asks you a question along the lines of:

<ul>
<li>Why did you apply to our company?</li>
<li>What are you looking for in your next job?</li>
<li>What makes you a good fit for our company?</li>
</ul>

How should you respond?

When asked 'What are you looking for in your next job?' in an interview, how can you tie the company's employee benefits into your response?

When asked 'What are you looking for in your next job?' in an interview, how can you tie the company's employee benefits into your response?

Why Do You Want to Work With Us I

How can company values be used effectively in an interview when asked 'What makes you a good fit for our company?'

How can company values be used effectively in an interview when asked 'What makes you a good fit for our company?'

Why Do You Want to Work With Us II

When responding to the question 'Why did you apply to our company?' during an interview, what aspect should you highlight?

When responding to the question 'Why did you apply to our company?' during an interview, what aspect should you highlight?

Why Do You Want to Work With Us III

How would you explain the concept of linear regression to three different audiences: a child, a first-year college student, and a seasoned mathematician? Ensure your explanations are tailored to the understanding level of each audience.

Describe linear regression to various audiences with different levels of knowledge.

Explaining Linear Regression to Different Audiences

What are the benefits of dynamic pricing, and how can you estimate supply and demand in this context?

Achieving maximum profits through dynamic pricing with real-time supply and demand fluctuations

Understanding Dynamic Pricing Strategy

Analytics

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

Explain the difference between the XGBoost and random forest algorithms and give an example where you would use one over the other.

Why might a data scientist choose to use XGBoost instead of Random Forest for a particular machine learning task?

Why might a data scientist choose to use XGBoost instead of Random Forest for a particular machine learning task?

XGBoost vs Random Forest: Choice

Explain the difference between XGBoost and random forest and give an example where you would use one over the other

Xgboost vs Random Forest

Let’s say you’re analyzing an AB test that has both a test group and a control group.

<ol>
<li>How do you calculate the sample size necessary for an accurate measurement?</li>

<li>Let’s say that the sample size is similar and sufficient between the two groups. In order to measure very small differences between the two, should the power get bigger or smaller?</li>
</ol>

Let’s say you work at an eCommerce startup. You run an A/B test on the company’s checkout product page with the hypothesis that surfacing free shipping will increase conversions.

The experiment group specifies whether the product qualifies for free shipping or not. In the control group, there is no specification of free shipping.

Let’s say we run the experiment for two weeks and get these results.

Control:

<ul>
<li>3056 page visits</li>
<li>1413 conversions</li>
<li>46% conversion</li>
</ul>

Experiment:

<ul>
<li>2947 samples</li>
<li>1533 conversions</li>
<li>52% conversion</li>
</ul>

How would you evaluate the results and if the test was successful or not?

How would you evaluate the results and if the test was successful or not

Free Shipping Mention Test

Let’s say we want to launch a re-design of a landing page to improve the click-through rate. We can do this by implementing an AB test.
Given that we launch an AB test, how would you infer if the results of the click-through rate were statistically significant or not?

How can you accurately conclude if the results of an A/B test, conducted to evaluate the effectiveness of a landing page redesign, are statistically significant?

How can you accurately conclude if the results of an A/B test, conducted to evaluate the effectiveness of a landing page redesign, are statistically significant?

Statistically Significant Test

Precisely ascertain whether the outcomes of an A/B test, executed to assess the impact of a landing page redesign, exhibit statistical significance.

Regularization and cross-validation are two common techniques used to improve the performance of machine learning algorithms.

When should you use one versus the other?

Given the choices below, which one is an example of a scenario where we should be using regularization?

Given the choices below, which one is an example of a scenario where we should be using regularization?

Regularization Example

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

What is the difference between Logistic and Linear Regression?

When would use one instead of the other in practice?

What is the difference between Logistic and Linear Regression?

Linear vs Logistic Regression

Challenge

<h3>Job Description</h3><div>Job Description<div><div>Stand Together is a philanthropic community that helps America’s boldest changemakers tackle the root causes of our country’s biggest problems, from education to the economy, broken communities, and toxic division, among dozens of other pressing issues. We provide our partners with access to resources including funding, thought leadership, a network of peers, and a playbook for applying proven principles to transform lives and society. </div><div> </div>As a Senior Digital Marketing Analyst for the MarComms team, you will be a strategic resource, fostering data-driven decisions and actionable insights that help Stand Together identify, educate, and mobilize a growing community of social entrepreneurs and leaders. Your expertise in content performance and customer journey analytics and your ability to craft clear, actionable insights for executive audiences will be critical in optimizing our decisions and learnings around content, journeys, and cross-media tactics and campaigns. Your work will help empower leaders to improve the effectiveness of our initiatives and outcomes.How You Will Contribute<ul><li>Work with business and capability leaders from Brand, Marketing Strategy, and other Digital marketing leads to establish and execute analytic measurement plans that capture critical data points along customer journeys and various cross-media tactics on our key initiatives.</li><li>Collaborate with other leaders and independently conduct data analysis to address critical business questions. Using your expertise in data storytelling and visualization, you will distill complex information into actionable insights that “connect the dots” for senior leaders, driving key decisions and outcomes. You will regularly share progress and insights through weekly, monthly, and post-project recaps with leadership across the Marketing and Communications organization (MARCOMMS) and our partners.</li><li>Use data from multiple marketing sources (e.g. Ad Platform Data, Google Analytics 4, Email, Segment,…) and leverage the latest cloud databases (Snowflake and BigQuery), and data visualization tools (Tableau) to create analysis, reports, and dashboards, that help communicate, educate, and connect stakeholders to trends, key learnings, and insights that help evaluate results.</li><li>Lead tag management across all Stand Together web properties using Google Tag Manager — including new container setup, GA4 implementation, marketing pixel deployment, and rigorous testing and QA. You'll be the primary owner of GA4 configuration and will ensure data collection is accurate, complete, and fit for purpose across all measurement use cases.</li></ul><div>Strategic Data Storyteller:</div><ul><li>Excel at distilling complex data into simplified, actionable insights. Your clear and concise narratives enable senior leaders to easily grasp the key takeaways from our digital efforts, empowering them to make informed, strategic decisions.</li></ul><div>Analytics Partner and Advisor: </div><ul><li>Collaborate with subject matter experts as driven by individual project needs to objectively identify and highlight key learnings around content, both paid and organic digital and community building activities to make both tactical and strategic recommendations that enable us to evaluate and optimize results and decisions impacting audiences, campaigns, content, and tactics. </li></ul><div> A GA4 and Tag Management Expert:</div><ul><li>Deep, hands-on experience with Google Analytics 4, both configuration and analysis, and are highly proficient in Google Tag Manager. You’re skilled in fine-tuning GA4 to track and analyze customer journeys, content performance, and campaign effectiveness to uncover key insights that support robust data-driven decisions. You're comfortable with container architecture, data layer implementation, custom event tracking, and cross-domain measurement. You can diagnose and resolve tracking issues independently and know how to QA implementations rigorously before go-live.</li></ul><div>Master of Content Analytics:</div><ul><li>Proven track record of analyzing content performance across various channels, including web, email, and social media. You excel at measuring the impact of content on audience engagement, conversions, and long-term business outcomes. Your insights help teams optimize content strategies to better meet organizational goals.</li></ul><div> </div><div>Team Player and Growth Advocate: </div><ul><li>Work within the team and with customer stakeholders to help identify opportunities to improve services, provide deeper analysis, and suggest common data or measurement tool enhancements that promote stakeholder value and efficient measurement.  You’ll advise, train, and/or mentor others in use and interpretation of results and tools that help extend the teams value and usage of the data tools created.</li></ul>What You Will Bring<ul><li>Bachelor’s or advanced degree in marketing, data science, statistics, or a related field.</li><li>At least 6 years in digital/content marketing analytics, business intelligence, or similar roles. You have a proven track record of using data to drive deep actionable insights and an ability to simplify and communicate these findings to Sr. Leaders.</li><li>Demonstrated proficiency in use of digital analytics tools and concepts, including:<ul><li>SEO/GEO/SEM & Web Analytics (Google Analytics 4, Google Ads, Bing)</li><li>Tag Managers (Google Tag Manager or equivalent)</li><li>Content Performance Analytics (video, web/email Articles, Blogs, etc.)</li><li>Social Media Analytics (Facebook, Instagram, X, YouTube, LinkedIn, TikTok)</li><li>Reporting/Data Visualization (Tableau, Looker)</li><li>Cloud Databases (Snowflake, BigQuery, or similar)</li><li>Email Marketing Analytics (Iterable, HubSpot)</li><li>Social Listening (Meltwater, Brandwatch, or other similar tools)</li><li>CRM/CDP platforms (Salesforce, Segment)</li></ul></li><li>Independent & Organized: You manage multiple projects effectively, prioritize work independently, and meet deadlines with minimal supervision.</li><li>Exceptional Communicator: Your ability to translate complex data into simple, easy to understand insights that helps senior leaders and stakeholders across various levels better connect the dots to understand and act on your recommendations.</li><li>Enthusiasm to contribute to Stand Together's vision and principled approach to solving problems, and a commitment to stewarding our culture, which champions values including transformation and innovation, entrepreneurialism, humility, and respect. </li></ul>What We Offer<ul><li>Competitive benefits: Enjoy a 6% 401(k) match with immediate vesting, flexible time off, comprehensive health and dental plans, plus wellness and mental health support through Peloton and Talkspace. </li><li>A meaningful career: Join a passionate community of over 1,300 employees dedicated to improving lives and driving innovative solutionsto complex social challenges. </li><li>Commitment to growth: Thrive in a non-hierarchical environment that empowers employees to discover, develop and apply their unique talents. </li><li>Competitive compensation: Our approach rewards the value you create through competitive salaries and bonus opportunities, allowing you to share in the success you help drive. </li></ul><div><div>For this role, the anticipated base salary range is $115,000–$135,000 per year. This role may also be eligible for additional bonus compensation designed to align total compensation with an employee’s overall contribution to the organization. </div><div>Our Principles-Based Management™ (PBM) compensation philosophy is grounded in an individualized approach that rewards the long-term value each person creates for the organization. Compensation decisions are informed by a variety of factors, including relevant experience, capabilities, demonstrated skills, role responsibilities, and expected contribution. Consistent with this approach, base salary may vary within or outside of the anticipated range based on these considerations. </div></div><div>Our Values: Working for an organization within the Stand Together philanthropic community is different from many other places. The culture is deeply rooted in Principle Based Management® (PBM®), a framework guided by the principles that drive human progress, such as dignity, openness, and bottom-up empowerment. PBM® empowers employees to be entrepreneurial, to innovate, and to continually drive transformation.   </div><div> </div><div>We believe diversity fuels creativity, broadens knowledge, and helps drive success.  That is why we’re proud to be an Equal Opportunity Employer and strive to treat all employees and applicants with honesty, dignity, and respect.</div>We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.</div></div>

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

Stand Together Interview Questions

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