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# Introduction

I work as a data scientist at a pretty large tech company. You know, the type of company that pays well, has flexible working hours, and an office that looks more like a trendy cafe than a corporate workspace (we have plush sofas and beanbags). My job at this company is a product data scientist.

Typically, big tech companies like Google, Meta, and Amazon hire product data scientists to help drive millions of dollars in revenue.

In fact, FAANG companies primarily hire product data scientists for their core teams, and these professionals are heavily compensated, often making more money than traditional data scientists. This is because product data scientists work closely with business teams and make decisions that impact millions of users on a daily basis.

I believe that in the age of AI, product data science roles are more secure than a traditional data science job. This is because the closer you are to influencing major business decisions, the harder you are to replace. While AI can build predictive models with decent accuracy, it can’t convince the VP of Product to kill a feature, and it simply cannot gain a deep enough understanding of a specific product to influence stakeholders.

But I digress.

You clicked on this article to learn about how to ace data science interviews at large tech companies, and I shall not make you wait any longer.

Here’s what I’ll explain to you in this article:

• What I do as a product data scientist.
• How I prepared for this product data science role, and what makes product data science different from other, traditional data science jobs.
• My 6-week preparation plan to ace this data science interview.
• What you should learn if you want to become a product data scientist (whether you already have some data skills or are a complete beginner).

# What I Do as a Product Data Scientist

In simple words, I use analytical techniques to answer questions like:

• Should we launch this new feature, and is it worth the investment?
• How much money can we potentially make from this new product launch?
• How do we utilize data to get users to engage more with the products and services we offer?
• How can we get people to spend as much time on the app as possible?

# How I Prepared for the Data Science Interview

// 1. Start with Core Data Science Skills

As we learned earlier in this article, product data science roles are different from traditional data science roles. Before applying to this job, I already had 2 years of work experience as a data scientist in forecasting at another company.

This means that I already had the following skills:

• Programming: I was comfortable with Python and used it for web scraping, data analysis, and visualization.
• Data Analysis: I knew how to perform EDA with tools like PowerBI and could tell stories with data.
• Machine Learning: I could build, train, and evaluate machine learning models. This includes simple regression models, along with more advanced topics like time series forecasting.

If you don’t already have these skills, I recommend watching my YouTube video on how to gain the foundational knowledge required to become a data scientist.

The above skills are easy to gain through self-study and will take about 4–6 months to acquire.

// 2. Additional Skills for the Product Data Science Interview

Product data science requires a slightly different set of skills than a traditional data science role. You don’t just build predictive models as a product data scientist; you have to understand the entire product ecosystem and help decide what features to build, what’s working well, and what to kill.

Here are the additional skills I’ve had to learn as a product data scientist:

→ SQL SQL is the primary language of a product data scientist. All this while (as a traditional data scientist), I had been working in Python notebooks, and nowadays I almost exclusively write SQL queries.

To learn SQL, I did two things. First, I took this SQL course for data analytics. Then, I spent 3 weeks solving SQL problems on LeetCode and HackerRank.

This practice was sufficient to get me through the technical portion of the interview.

→ Statistics for Decision Making I already knew statistics and had taken multiple courses on it. But as a product data scientist, I had to learn the skill of applied statistics. This means I had to use a programming language to find the confidence interval of a feature.

If a feature (like adding a pop-up to the screen) led to more engagement with a specific confidence interval, I had to decide whether the product was worth launching. I also had to understand concepts like how to choose the correct sample population for our experiment to ensure that our results are unbiased.

If these concepts sound foreign to you, I’d suggest taking this free course on inferential statistics by Udacity. This, along with Udacity’s free A/B testing program by Google, helped me answer the statistics and product-related interview questions for this role.

→ Bridging the Gap Between Math and Business A huge part of product analytics is essentially bridging the gap between math and business. You decide on a success metric for a specific product, and if the product performs well, you launch it. For example, if your success metric is Click-Through-Rate (CTR), you might say something like:

“A 2% improvement in CTR leads to an additional $1.5M in revenue annually, so we should ship this feature.”

Of course, the above example is overly simplistic, as product teams often generate multiple complex metrics to capture different elements of user engagement.

Questions related to metric formulation and business use cases were the most difficult ones to answer during the interview. To prepare for this, I skimmed through this product analytics course on Coursera (although I didn’t complete it).

# My Data Science Interview Process: Key Takeaways

To summarize, my product data science interview tested me on the following skills:

• Timed SQL challenges.
• Experiment design and statistics: “How would you build the sample population for this experiment, and how will you decide on an experiment duration?”.
• Business and product knowledge: “Our current metric captures the number of sessions that cannot find their desired result on the first search results page. However, it doesn’t consider whether users had purchase intent or if they were just browsing. How would you refine this metric to capture ‘true search failure’?”.

The resources and interview questions I’ve shared in this article have helped me land this data science role. Having worked as a data scientist for multiple years, I’ve learned that product data scientists are essentially business strategists who know how to work with data.

Since we work so closely with business teams to make decisions that directly impact the company’s bottom line, I believe that this role is extremely valuable in an era where AI can handle routine modeling and analysis. If you’re thinking about becoming a data scientist, or even if you already are one, I strongly suggest considering the product data science route.

Yes, this role is more competitive since these roles are primarily offered by larger tech companies and product-centric organizations. However, if you invest time and effort into preparing for a role like this, it puts you at the center of important business decisions, naturally leading to increased compensation and career security.

Natassha Selvaraj is a self-taught data scientist with a passion for writing. Natassha writes on everything data science-related, a true master of all data topics. You can connect with her on LinkedIn or check out her YouTube channel.