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# Introduction
I work as a data scientist at a pretty big tech company. You know, the kind of company that pays well, has flexible work hours, and an office that looks more like a trendy cafe than a corporate workspace (we have plush couches and bean bags). My job in this company is one Product Data Scientist.
Typically, big tech companies like Google, Meta, and Amazon hire product data scientists to help generate millions of dollars in revenue.
In fact, Fang companies primarily hire product data scientists for their core teams, and these professionals are highly compensated, often earning more money than traditional data scientists. That’s because product data scientists work closely with business teams and make decisions that affect millions of customers on a daily basis.
I believe that in the age of AI, product data science roles are more secure than traditional data science jobs. This is because the closer you are to influencing major business decisions, the harder it is to replace you. While AI can build predictive models with decent accuracy, it can’t convince a VP of product to hit a feature, and it can’t gain a deep understanding of a particular product to influence stakeholders.
But I dig.
You clicked on this article to learn how to ace data science interviews at big tech companies, and I won’t keep you waiting any longer.
Here is what I will explain to you in this article:
- What I do as a Product Data Scientist
- How did I prepare 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 Crack This Data Science Interview
- What you should learn if you want to become a product data scientist (whether you already have data skills or are a complete beginner).
# What I do as a Product Data Scientist
Simply put, 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 use data to better engage customers with the products and services we offer?
- How can we get people to spend more time on the app?
# How I Prepared for Data Science Interview
// 1. Start with basic data science skills
As we learned earlier in this article, product data science roles are different from traditional data science roles. Before applying for 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 PowerB and could tell stories with data.
- Machine Learning: I can build, train and evaluate machine learning models. It includes simple regression models along with more advanced topics such as time series forecasting.
If you don’t already have this skill, I recommend looking My YouTube video How to acquire the basic knowledge needed to become a data scientist.
The above skill is easy to acquire through self-study and will take around 4-6 months to acquire.
// 2. Additional Skills for Product Data Science Interviews
Product data science requires a slightly different skill set than traditional data science roles. You don’t just build predictive models as a product data scientist. You need to understand the entire product ecosystem and help decide what features to build, what’s working, and what to kill.
Here are additional skills I had to learn as a product data scientist:
→ SQL
SQL is the primary language of the product data scientist. All this (as a traditional data scientist), I was working in a Python notebook, 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 Late code And Hackerwink.
This process was enough to get me through the technical part of the interview.
→ Decision making statistics
I already knew statistics and had taken several courses on it. But as a product data scientist, I had to learn the skills Applied statistics. This means I had to use a programming language to find the confidence interval of a characteristic.
If a feature (like adding a pop-up to the screen) results in more engagement with a certain confidence interval, I have to decide if the product is worth launching or not. I also had to figure out how to choose the right sample population for our experiment to make sure our results were unbiased.
If these concepts seem foreign to you, I would suggest taking This free course On extraordinary statistics by udacity. Along with that udacity’s free A/B testing program Through Google, helped me answer interview questions related to statistics and product for this role.
→ Bridging the gap between math and business
A big part of product analytics is essentially bridging the gap between math and business. You decide on a success metric for a particular 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 $1.5 million in annual revenue, so we should ship this feature.”
Of course, the above example is oversimplified, as product teams often develop multiple complex metrics to capture different elements of user engagement.
Questions related to metric creation and business use cases were the most difficult during the interview. To prepare for this, I skimmed through it This Product Analytics course on Coursera (I didn’t finish it though).
# My Data Science Interview Process: The Key Path
To summarize, my Product Data Science interview tested me on the following skills:
- Timed SQL Challenges.
- Experiment Design and Statistics: “How will you construct the sample population for this experiment, and how will you decide on the experimental 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 does not take into account whether users have purchase intent or are just browsing. How would you improve this metric to capture ‘true search failure’?”
The resources and interview questions I shared in this article helped me land this data science role. After working as a data scientist for several years, I’ve learned that product data scientists are primarily business strategists who know how to work with data.
Because we work so closely with business teams to make decisions that directly affect the company’s bottom line, I believe 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 recommend considering the product data science route.
Yes, this role is more competitive as these roles are mainly offered by large tech companies and product centric organizations. However, if you put the time and effort into preparing for such a role, it puts you at the center of important business decisions, which naturally increases compensation and career security.
Natasa Seluraj A self-taught data scientist with a passion for writing. Natasa writes on all things data science, a true master of all data topics. You can contact him LinkedIn Or check it out YouTube channel.