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Machine Learning

What are some reasons why measuring bias would be important in building this particular model

Food Delivery Times

Which model do you pick given 85% and 82% accuracy

85% vs 82%

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

Spam Classifier 

How would you evaluate whether a decision tree is the right model and assess its performance before and after deployment

Decision Tree Evaluation

How to model merchant acquisition in a new market?

Merchant Acquisition

Product Sense & Metrics

How would you measure the success of Facebook Groups

Group Success

How would you measure the success of the Instagram TV product

Instagram TV Success

What data points and metrics would you look at to decide if amateur creators are doing worse and only superstars do well

Amateur Performance

What are the top five metrics you would start tracking to understand the health of Google Docs

Docs Metrics

What are the most important metrics for WhatsApp

WhatsApp Metrics

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

Why Do You Want to Work With Us

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

Data Pipelines

Design a data pipeline to compute hourly, daily, and weekly active user metrics from a data lake for an hourly-refreshing dashboard

Data Pipelines and Aggregation

How would you build an ETL pipeline to ingest Stripe payment data into a warehouse for revenue dashboards and analytics

Payment Data Pipeline

How would you build a reusable data cleaning pipeline for messy user event data?

User Event Data Pipeline

Design a reporting pipeline for a major tech company using only open-source tools under strict budget constraints.

Open Source Reporting Pipeline

Business Case

Diagnose why the pricing algorithm is under-pricing a consumer product

Underpricing Algorithm

Demystifying data for non-technical users through visualization and clear communication

Accessible Data

Let’s say we have a sample size of \(n\). The margin of error for our sample size is 3.

How many more samples would we need to decrease the margin of error to 0.3?

Let's say we have a sample size of N. The margin of error for our sample size is 3.

How many more samples would we need to decrease the margin of error to 0.3?

Let's say we have a sample size of N. The margin of error for our sample size is 3.

How many more samples would we need to decrease the margin of error to 0.3?

Reducing Error Margin

How many more samples are needed to decrease the margin of error to 0.3

Statistics

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

Explain what a p-value is to someone who is not technical

P-value to a Layman

Let’s say you’re a Product Data Scientist at Instagram. How would you measure the success of the Instagram TV product?

What is the primary goal of Instagram TV as a product feature?

What is the primary goal of Instagram TV as a product feature?

How would you measure the success of Facebook Groups?

Considering the purpose of Facebook Groups as a platform for connecting users through shared interests or real-life relationships, which metrics would be appropriate to measure the success of these groups?

Considering the purpose of Facebook Groups as a platform for connecting users through shared interests or real-life relationships, which metrics would be appropriate to measure the success of these groups?

Let’s say you work at Allstate. Allstate is running <code>N</code> online ads right now. The table <code>ads</code> contains all those ads, ranked by popularity via the <code>id</code> column (e.g., the entry with <code>id = 1</code> is the most popular, etc.).

Create a subquery or common table expression named <code>top_ads</code> containing the top 3 ads (by popularity) and return the number of rows that would result from the following operations

<ol>
<li><code>ads INNER JOIN top_ads</code></li>
<li><code>ads LEFT JOIN top_ads</code></li>
<li><code>ads RIGHT JOIN top_ads</code></li>
<li><code>ads CROSS JOIN top_ads</code></li>
</ol>

Note: Please make the <code>join_type</code> column in your output have the values <code>inner_join</code>, <code>left_join</code>, etc. for each of their respective join types

Note: Please return only one query with each number in a different row

Example:

Input:

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

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

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
Output:

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

<tbody>
<tr>
<td><code>join_type</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>number_of_rows</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

Create top_ads with the top 3 ads and return the row counts for inner, left, right, and cross joins with ads

Size of Joins

You are a data scientist at YouTube focused on creators.

A PM comes to you worried that amateur video creators could do well before but now it seems like only “superstars” do well.

What data points and metrics would you look at to decide if this is true or not?

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?

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

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

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.

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

Success Measurement

Let’s say that you work as a data scientist at a food delivery company.

You are tasked with building a model to predict food preparation times at a restaurant from the time when a customer sends in the order to when the meal is ready for the driver to pick up.

What are some reasons why measuring bias would be important in building this particular model?

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

Let’s say you have analytics data stored in a data lake. An analyst tells you they need hourly, daily, and weekly active user data for a dashboard that refreshes every hour.

How would you build this data pipeline?

How would you handle the data preparation for building a machine learning model using imbalanced data?

Addressing imbalanced data in machine learning through carefully prepared techniques.

Data Preparation for Imbalanced Data

Let’s say you’re working with a dataset of user-level events for a new product feature, and you notice issues like missing values, duplicate records, inconsistent data types, and some clear outliers. You need to prepare this data for training a machine learning model.

How would you design a data processing pipeline using Pandas and NumPy to clean and validate the data before modeling, and what steps would you take to ensure the pipeline is reusable and reliable for future use?

Let’s say that you’re a data scientist working for Facebook.

You’ve been asked to generate a machine learning model that can map the legal first name of a person to likely nicknames they might have.

How do you go about designing this model?

Mapping Nicknames

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

Let’s say we’re working on a new feature for LinkedIn chat. We want to implement a green dot to show an “active user” but given engineering constraints, we can’t AB test it before release.

How would you analyze the effectiveness of this new feature?

How would you analyze the effectiveness of this new feature?

Green Dot

A/B Testing

How would you measure success for Facebook Stories?

Measure Facebook Stories success by tracking reach, engagement, and actions aligned with specific business goals

Facebook Story Success

You work as a data engineer at Spotify. The executive team requests a weekly analytics report summarizing user engagement and streaming trends, using data from internal databases and log files. Strict budget constraints prohibit the use of commercial or managed cloud services; you must rely solely on open-source technologies such as PostgreSQL, Apache Airflow, Apache Spark, and Metabase.
How would you architect this reporting pipeline from data ingestion to report delivery?

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

To improve customer experience on Uber Eats, what key parameters would you focus on improving?

Delivering an exceptional customer experience by focusing on key customer-centric parameters

Uber Eats Customer Experience

Let’s say you work as a data scientist at a bank.

You are tasked with building a decision tree model to predict if a borrower will pay back a personal loan they are taking out.

<ol>
<li>How would you evaluate whether using a decision tree algorithm is the correct model for the problem?</li>

<li>Let’s say you move forward with the decision tree model. How would you evaluate the performance of the model before deployment and after?</li>
</ol>

Let’s say we’re responsible for deploying a new version of Uber’s routing system, and the release includes both backend and frontend changes that may impact user workflows.

How would you design the deployment process to minimize risk, ensure clear communication with both clients and internal teams, and handle rollbacks if unexpected issues arise?

How would you structure a safe deployment process with effective stakeholder communication and rollback plans?

Safe Deployments

DevOps

Let’s say that you’re in charge of getting payment data into your internal data warehouse.

How would you build an ETL pipeline to get Stripe payment data into the database so analysts can build revenue dashboards and run analytics?

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

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.

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

Risk Assessment Model

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

Let’s say you work as a data scientist at DoorDash. 

You are tasked to build a machine learning system that minimizes missing or wrong orders placed on the app.

How would you go about designing this system?

Minimize Wrong Orders

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

Let’s say every year, PG&amp;E has to forecast exactly how much electricity to supply a town. We can’t supply too little or else it causes outages but if we supply too much, it’ll waste money if it’s not consumed by the town. 

What’s one way we can model out how much electricity to supply?

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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