Finding Success as a Data Scientist in Tech (Part 1): Developing Product Sense
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Do you know what’s one key thing big tech companies like Spotify, Netflix, and Facebook (Meta) have in common with the majority of tech startups out there?
Besides the ping-pong tables and overpriced coffee machines, of course
From the big to the small, they are all built around a product
Most importantly, at least for us data people
They all have—or at least try to have—a data-driven approach to product development.
Now, what does that mean for those of you hoping to work and thrive as Data Scientists in tech?
Simple: Opportunity
Ask yourself:
Do I want to help drive more business growth?
Do I want to fast-track my way to promotions and raises?
Do I want to become a more competitive candidate in the job market?
If you answered yes to any of these questions, then you want to develop a strong product sense
Working in tech over the past 5 years has made it clear how crucial this skill is in driving success.
But what does product sense actually mean and how can you develop it?
Let’s dive into it!
💡 This is part 1 of a 3-part series of articles where I’ll share everything I know to help you thrive as a Data Scientist in tech.
Demystifying Product Sense
Product sense is the ability to do two things:
Empathy: Intuitively understand what makes a product valuable to users
Execution: Make strategic decisions that improve the product's overall success.
And for us Data Scientists, product sense means stepping outside the data and seeing how it fits into the broader picture—connecting insights to the company’s product and its users.
In other words:
💡 It’s not just about generating insights but about delivering the right insights—ones that help the team (Product Managers, Tech leads, executives, etc…) make better product decisions.
The Main Focus for Data Scientists
When it comes to product sense, execution is where data scientists truly shine. And honestly, it’s probably the most important skill to master.
Why? Two main reasons:
Before the job: It’s what you’ll be grilled on during technical interviews
During the job: It’s where most of your focus will be once you’re working in tech, especially in a role as a Product Data Scientist.
Let’s talk about each
1—Before the job
In interviews, execution often comes up when you're asked to think through how you’d solve a real product problem.
For example, you might get a scenario where a product feature isn’t performing as expected.
💡 Your job is to figure out how to analyze the data, understand what’s going wrong and offer a data-backed recommendation to fix it.
This is why developing a strong product sense is important even before you are on the job.
2—During the job
As a Product Data Scientist, your entire role revolves around executing on data insights that drive product improvements.
💡 Whether it’s running an experiment or analyzing a dip in user retention, the key is being able to act on the data you have and deliver recommendations that push the product forward.
But knowing how to execute is only part of the equation. To make a real impact, your work needs to be aligned with the right goals.
Aligning with Success Metrics
To truly make an impact as a data scientist, your work needs to be aligned with the company’s success metrics—the key numbers that drive the business forward.
Whether you’re working in product, marketing, or operations, your analysis is only as valuable as its relevance to the company’s key objectives.
Every team has metrics they care about. It usually looks something like this:
User retention for the product team
Conversion rates for marketing
Efficiency for operations.
Your job is to ensure that your work contributes to improving those metrics.
For example, say your product team is focused on improving user retention.
As a data scientist, your job is to dive into the data and figure out what’s driving users to drop off:
Are they getting stuck at a particular stage?
Are certain features turning users away?
Once you’ve found the patterns, you can offer data-driven solutions that actually move the needle.
Putting It All Together: Product Sense in Action
We’ve talked about execution, success metrics, and why they matter.
Now, you are probably wondering: How can I actually apply all of this?
Let me break it down for you into two practical ways:
1—Asking the right questions
Whether you're working on a current project at work or thinking ahead to your future role, ask yourself:
What’s the goal of this project?
What success metrics are we trying to impact?
What data can I use to move the needle?
💡 Keeping these questions in mind and constantly iterating on your analysis, you’re not just delivering insights—you’re driving the product forward, and that’s the key to thriving as a data scientist in tech.
2—Key technical skills
When applying product sense as a data scientist, the two most crucial technical skills to master are: Event tracking and A/B testing.
These are key to helping you understand user behavior and provide actionable insights that align with success metrics.
Event Tracking: Event tracking helps you monitor key actions users take, like clicks, sign-ups, or feature usage. This data shows you what users are actually doing and helps you optimize their experience.
A/B Testing: It's one of the most effective ways to validate assumptions, whether you're working on improving a product feature or boosting marketing performance. The key is to design experiments that directly impact success metrics, such as conversion rates or feature adoption.
💡 By mastering these two technical skills you’ll not only understand what works but also have the data to back it up.
Closing thoughts
The moment I stopped thinking of data as isolated numbers and metrics and instead considered how my work fit into the larger picture, was the moment I started driving business growth.
It didn’t fully click for me until a few years into my career, but in hindsight, this shift was one of the key factors behind my raises and promotions.
Luckily for you, you don’t have to wait years.
You can start building these skills and thriving as a data scientist in tech, starting today!
Thank you for reading! I hope these tips will help you get on your way to a successful career in tech.
See you next week!
- Andres
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These are great insights that are never thought in any Data Science program. It sounds very impactful.
I love how you broke down product sense into empathy and execution. Such a neat way to explain it