Hi, everyone! My name is Basty, and I’m a data scientist and educator from Metro Manila. If there’s something I value so much, that is education. Education has allowed me to not only dive into the amazing world of code and data but also to encourage and inspire others to do the same. Read more about me here.
Outside of work and school, I love playing video games like Valorant and League of Legends. I also love listening to Broadway musicals (HAMILTON, DEH, TICK TICK BOOM ALL THE WAY!). Lastly, I LOVE watching Friends, New Girl, HIMYM, and The Big Bang Theory.
Now, let’s take a look at my notebook!
Product Data Science? Never heard of it.
Data science is such an umbrella term that it can be a real puzzle, with different pieces coming together to create a bigger picture. In the Philippines, when we talk about data science, we often hear about the “traditional” machine learning and model building, especially within boot camps and even at job interviews. What most newcomers and even some data professionals don’t know is that there’s another side to this umbrella term, and we call it Product Data Science. So in this blog, join me as we discuss what Product Data Science is, and how it’s different from Traditional Data Science. Let’s get started!
There’s a high chance that you’ve never heard of this type of role before, and the type of businesses we have in the Philippines can be a factor in that. Most of the businesses we have in the country offer services rather than having their own product. So what is Product Data Science, and what does one do in this type of role?
In product development teams (mostly in startups), you have different people working together to build and improve the app. Usually, you have the designers, developers, product managers, then a data scientist. All these people work towards the goal of improving the app, and the goal of a product data scientist is to:
1) Work closely with product teams to identify potential improvements to a product or feature, and
2) Work with engineers to create and execute experiments (A/B tests).
In order to achieve these goals, Product Data Scientists dig deep into user data to uncover the secrets behind what keeps people engaged with the company’s product—examining user behavior and feedback. All the insights that they get from the data will then be presented to Product Managers and Owners, and eventually give validation as to whether a product feature should be released or not.
The questions that Product Data Scientists answer are also quite different from the typical data science questions we encounter. A Product Data Scientist might be asked questions like:
How can we give users a smoother ride experience?
How does the new landing page design impact our engagement and conversion?
How do you measure the success of a new feature?
Well, these questions don’t really show what a Product Data Scientist does, right? Most of the time, you’ll be closely monitoring product analytics to see how your app is performing. Speaking of product analytics, you’ll also be working closely with Product Managers to set Key Performance Indicators (KPIs) and metrics to track to measure success, such as Daily Active User (DAU) and retention. Interestingly, you’ll also find yourself working with Marketing and Growth teams to come up with strategies to optimize your marketing and acquisition plans. Perhaps the most defining task that differentiates Product Data Science from typical Data Science is designing and running A/B tests to see what works and what doesn’t.
So how is this role different from traditional Data Science, which often revolves around machine learning and model building? Both roles are geared towards creating impact for the business, but traditional Data Science often concentrates on solving business problems through building and fine-tuning machine learning models. Product Data Science, on the other hand, concentrates on enhancing user experience and driving product improvements through analytics and statistics.
Traditional Data Scientists also primarily work with other data scientists, data engineers, and domain experts and may somewhat be isolated from the product development process. On the other hand, product data scientists are deeply integrated into product development teams as they collaborate closely with designers, product managers, and developers.
Both roles are at the forefront when it comes to metrics, but success is measured differently for both of them. In traditional data science, you’ll mostly hear the terms precision, accuracy, recall, and F1 as they focus on the performance of the model. In product data science, metrics such as daily active users, retention, conversion rates, and user satisfaction play a more important role when it comes to measuring success.
By now, I hope you have a good understanding of what product data science is and what a product data scientist role is about. I also hope that you have garnered great appreciation for the diversity within the data science space and recognize that there’s more than one way to crack the data science code. And while traditional data science may be more apparent in the Philippines, don’t let that stop you from carving your path to do the work you love. Whether you're drawn to the algorithms and models of traditional data science or the user-centric, product-enhancing role of Product Data Science, there's a piece of the data science puzzle waiting for you to explore. Always remember that artists don’t only draw, developers don’t only write code, and data scientists don’t only do statistics and Machine Learning.
Never stop learning!