Marketing Data Scientist Interview Prep (+ Case Examples)

🎯 What is a Marketing Data Scientist?

A marketing data scientist specializes in analytics and statistical modeling that optimizes in brand awareness and marketing campaigns. The ultimate aim as a marketing data scientist is to acquire and retain users while increasing the lifetime value of the users. FAANGs, mid-sized companies, startups and consulting firms (e.g. BCG, McKinsey) - all employ marketing data science to expand reach and acquire customers.

And, they are particularly interested in interactions on marketing channels (e.g. email, social media Ads, TV commercials) and sales pages to customer conversion. Along the way, they will employ KPI tracking, experimentation and statistical modeling to optimize this process.

Real-World Projects (+ Interview Cases)

As an example, a marketing data scientist could get involved in the following projects. And, these are the type of problems you could expect in case interviews.

• Measurement - On the CashApp, how do you measure success of marketing campaigns in acquiring new customers? How would you construct a dashboard to tracks the success KPIs to business stakeholders?
• Opportunity Sizing - Suppose Facebook launched Portal (assuming it doesn’t exist yet), how many do we expect to sell this year?
• Lifetime Value Estimation (LTV) - How would you estimate the lifetime value of Slack’s premium subscribers?
• Attribution Modeling - Suppose that PayPal’s merchant team wants to streamline the user journey from marketing channel engagement to page engagement on its sales website and app. The aim is to increase conversions of PayPal products and services to merchants. How would you optimize the journey?
• Market Mix Modeling - Suppose Uber wants to optimize spends across online and offline marketing channels (e.g. push notification, social media campaigns, emails, in-app banner, banner, and TV commercials) to increase rides ordered, how would you achieve this?

There are, of course, other areas of problems a marketing data scientist may work on. And, these include experimentation involving AB testing, Causal Inference and Uplift Modeling. Much of the content highlighted above, you can get access to the conceptual deep-dive and case questions with detailed solutions on ultimate prep program.

Qualifications You Need (+ Areas Assessed in Interviews)

As a marketing data scientist, a typical requirement is that you at least have a Bachelor’s degree is quantitative and/or computer science field. Master’s is generally preferred. Having industry experience in product and/or marketing is also a great bonus.

In addition, you will be assessed on the following skills:

• SQL and/or Python
• Data Visualization
• Statistical Analysis and Modeling
• Communication Skills
• Stakeholder Management

💡 What this means is that, the areas highlighted above would be assessed in technical and onsite screens. In some cases, you may receive take-home assignments that involve you to analyze dataset, build a model, and present the findings in a powerpoint deck.

✍️ Understanding the Marketing Domain

As a marketing data scientist, you need to understand the business domain you are involved in. Ultimately, you are primarily involved in the top-funnel acquisition, activation, and retention process of a product. And, this process needs to be tracked in a KPI dashboard for product, marketing and business stakeholders to strategize and execute marketing goals. Here’s a funnel that marketing data scientist is often concerned with.

Here’s a sample of what a dashboard could look like - primarily focused on traffic acquisition of various mediums (e.g. direct link, social, video, search) to a user landing on a conversion page.

And, as a marketing data scientist, you will be concerned with the following set of KPIs

1. Customer Acquisition Cost (CAC): The average cost to acquire a new customer, taking into account marketing and sales expenses.
2. Customer Lifetime Value (CLTV or LTV): The projected revenue a single customer will generate over the course of their relationship with a business.
3. Retention Rate: The percentage of customers who continue to use a product or service over a specific time period.
4. Churn Rate: The percentage of customers who stop using a product or service over a specific time period.
5. Conversion Rate: The percentage of visitors (to a website, landing page, or app) who take a desired action, such as making a purchase or signing up for a newsletter.
6. Click-Through Rate (CTR): The ratio of users who click on a specific link to the number of total users who view an advertisement or page.
7. Cost Per Click (CPC): The average cost paid for each click in a pay-per-click (PPC) marketing campaign.
8. Return on Advertising Spend (ROAS): The revenue generated for every dollar spent on advertising.
9. Average Order Value (AOV): The average amount of money a customer spends per order.
10. Net Promoter Score (NPS): A measure of customer loyalty and satisfaction, determined by asking customers how likely they are to recommend a company to others.
11. Engagement Rate: A measure of user interactions with content, often used in the context of social media. It can include metrics like likes, shares, comments, and time spent on a page.
12. Bounce Rate: The percentage of visitors who navigate away from a website after viewing only one page.
13. Open Rate: In email marketing, the percentage of recipients who open an email from a campaign.
14. Multichannel Attribution: A method to determine which marketing channels (e.g., email, social media, search) contribute most to conversions or sales.
15. Segmentation Metrics: Metrics related to specific customer segments or cohorts, analyzing behaviors, and responses of these defined groups.
16. Sentiment Analysis: A qualitative metric often derived from analyzing customer reviews, feedback, or social media to gauge overall sentiment (positive, negative, neutral) toward a brand or product.
17. Traffic Sources: Analyzing where website traffic comes from, such as direct visits, search engines, referrals, or social media.
18. Sales Funnel Drop-Off Rate: The percentage of potential customers who exit at various stages of the sales or conversion funnel.
19. Campaign-Specific Metrics: Depending on the type of campaign (e.g., social media, PPC, email), there might be specific KPIs that are tracked, like ad impressions, video views, or affiliate clicks.
20. Referral Traffic: The number of visitors who come to your site from direct links on other websites rather than directly or through searches.

📚 Interview Questions

In the marketing data scientist interview, you will typically be assessed on the following areas:

📕 Python / SQL

📘 Modeling

📙 Marketing / Product Sense

📗 Culture-Fit

These are areas that are peppered throughout the technical and onsite interview process - each round typically being 45 to 60 minutes depending on the company.

📕 Python / SQL

You will be assessed on data manipulation that involves SQL and/or Python. The environment will be on a coderpad, or word document. You may or may not be allowed to execute the query when you write the solution. It depends on the interviewer and company. Note that the syntax or the choice of SQL type (MySQL vs PostgreSQL) doesn’t matter as much. What is more vital is the logic.

You are a marketing data scientist for Amazon. You have two tables:

Campaigns table that tracks all the marketing campaigns. The schema for this table is:

- campaign_id (unique identifier for each campaign)
- campaign_name (name of the campaign)
- channel (e.g., 'Email', 'Social Media', 'PPC')
- start_date (start date of the campaign)
- end_date (end date of the campaign)

Sales table that tracks all the sales made. The schema for this table is:

- sale_id (unique identifier for each sale)
- campaign_id (which campaign led to the sale, can be NULL if the sale wasn't driven by a marketing campaign)
- amount (total amount of the sale)
- sale_date (date the sale was made)

1) Write an SQL query to identify the top 3 marketing channels (channel from the 
campaigns table) in terms of total sales amount (amount from the sales table) 
for the sales they drove, but only consider sales that occurred during the 
duration of their respective campaigns (start_date to end_date). 
Display the marketing channel and the total sales amount for each.

2) Now, solve this problem using Pandas

SQL Solution

WITH ChannelSales AS (
    SELECT
        c.channel,
        SUM(s.amount) AS total_sales
    FROM
        campaigns c
    JOIN sales s ON c.campaign_id = s.campaign_id
    WHERE
        s.sale_date BETWEEN c.start_date AND c.end_date
    GROUP BY
        c.channel
)

SELECT
    channel,
    total_sales
FROM
    ChannelSales
ORDER BY
    total_sales DESC
LIMIT 3;

Pandas Solution

# Create dataframes
df_campaigns = pd.DataFrame(data_campaigns)
df_sales = pd.DataFrame(data_sales)

# Merge the dataframes on campaign_id
merged_df = df_sales.merge(df_campaigns, on='campaign_id', how='inner')

# Filter rows where sale_date is within the campaign duration
filtered_df = merged_df[(merged_df['sale_date'] >= merged_df['start_date']) & 
                        (merged_df['sale_date'] <= merged_df['end_date'])]

# Group by channel and sum the sales amounts
channel_sales = filtered_df.groupby('channel').amount.sum()

# Sort by total sales and get the top 3 channels
top_channels = channel_sales.sort_values(ascending=False).head(3)

print(top_channels)

📘 Modeling Case

The modeling cases are often assessed in take-home and onsite rounds. Your ability to employ regression and classification models (e.g. OLS, logistic regression, random forest, clustering) to solve marketing cases are vital. You do not need to know advanced algorithms like neural networks. Rather, it’s the application of simple models for complex problems is what is more vital. Here are some sample cases. If you want to see solutions to these problems, you can visit ultimate prep program.

1. Lifetime Value Estimation (LTV) - How would you estimate the lifetime value of Slack’s premium subscribers?
2. Attribution Modeling - Suppose that PayPal’s merchant team wants to streamline the user journey from marketing channel engagement to page engagement on its sales website and app. The aim is to increase conversions of PayPal products and services to merchants. How would you optimize the journey?
3. Market Mix Modeling - Suppose Uber wants to optimize spends across online and offline marketing channels (e.g. push notification, social media campaigns, emails, in-app banner, banner, and TV commercials) to increase rides ordered, how would you achieve this?

📙 Marketing / Product Sense

Marketing / Product Sense are assessment of your domain knowledge on marketing, KPIs, experimentation and metric investigation. Here are some sample questions. If you want to see solutions to these problems, you can visit ultimate prep program.

1. Measurement - On the CashApp, how do you measure success of marketing campaigns in acquiring new customers? How would you construct a dashboard to tracks the success KPIs to business stakeholders?
2. Opportunity Sizing - Suppose Facebook launched Portal (assuming it doesn’t exist yet), how many do we expect to sell this year?
3. Experimentation - Suppose Facebook changes the push notification algorithm. A push notification is a banner that’s displayed on the phone of a Facebook user. How would you design an experiment to evaluate whether the new notification algorithm is more effective than the old one?

📗 Culture-Fit

Culture-fit is typically assessed in a conversation with hiring lead and/or director of the marketing operation. There will be a round dedicated on assessing the behavioral and cultural aspect of your candidacy. Here are some culture-fit questions.

1. About the Company and Role:
   • How do you feel our company's values align with your personal values?
   • What excites you about the intersection of marketing and data science?
   • Why did you choose to apply to our company specifically for a marketing data science role?
2. Collaboration and Teamwork:
   • Describe a time when you worked on a cross-functional team. What challenges did you face, and how did you overcome them?
   • How do you handle disagreements or differences of opinion within a team, especially when you believe your data-driven insights are accurate?
   • In a scenario where the marketing team has a different perspective or interpretation of the data than you do, how would you approach this?
3. Communication:
   • Describe a situation where you had to explain a complex data-driven insight to a non-technical audience. How did you ensure they understood?
   • What methods do you use to ensure clear communication when presenting your findings to stakeholders or team members?
4. Problem Solving and Critical Thinking:
   • When faced with a marketing challenge that doesn't have a clear data-driven solution, how do you approach it?
   • How do you prioritize tasks or projects when everything seems to be a top priority?
5. Company Culture:
   • What type of work environment do you thrive in, and why?
   • How do you handle stress or tight deadlines, especially in a fast-paced marketing environment?
   • What role do you usually take on in a team setting? How do you feel about leadership or taking the initiative?

💡 Preparation Tips

Tip 1 - Build intimacy with the company and product you are interviewing for.

How can you interview for a company if you don’t understand their core product? You need a strong grasp of the interview itself. As you are getting ready for the interview, use this product breakdown to become familiar with the business model and its product. This knowledge will become the basis in solving challenging marketing sense and business case problems.

Tip 2 - Understand how technical screens work.

The typical format is 20 to 40 minutes embedded in a technical phone screen or in the onsite round. You will be assessed by Sr./Staff-level data scientist. Here’s a sample video. You can also get coaching with a data scientist at FAANG companies: https://www.datainterview.com/coaching

Tip 3 - Join the Ultimate Prep

Get access to ML questions, cases and machine learning mock interview recordings when you join the ultimate prep program.

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