At Shopify, we've embraced the idea of full stack data science and are often asked, "What does it mean to be a full stack data scientist?". The term has seen a recent surge in the data industry, but there doesn’t seem to be a consensus on a definition. So, we chatted with a few Shopify data scientists to share our definition and experience.
What is a Full Stack Data Scientist?
"Full stack data scientists engage in all stages of the data science lifecycle. While you obviously can’t be a master of everything, full stack data scientists deliver high-impact, relatively quickly because they’re connected to each step in the process and design of what they’re building." - Siphu Langeni, Data Scientist
"Full stack data science can be summed up by one word—ownership. As a data scientist you own a project end-to-end. You don't need to be an expert in every method, but you need to be familiar with what’s out there. This helps you identify what’s the best solution for what you’re solving for." - Yizhar (Izzy) Toren, Senior Data Scientist
Typically, data science teams are organized to have different data scientists work on singular aspects of a data science project. However, a full stack data scientist’s scope covers a data science project from end-to-end, including:
- Discovery and analysis: How you collect, study, and interpret data from a number of different sources. This stage includes identifying business problems.
- Acquisition: Moving data from diverse sources into your data warehouse.
- Data modeling: The process for transforming data using batch, streaming, and machine learning tools.
What Skills Make a Successful Full Stack Data Scientist?
"Typically the problems you're solving for, you’re understanding them as you're solving them. That’s why you need to be constantly communicating with your stakeholders and asking questions. You also need good engineering practices. Not only are you responsible for identifying a solution, you also need to build the pipeline to ship that solution into production." - Yizhar (Izzy) Toren, Senior Data Scientist
"The most effective full stack data scientists don't just wait for ad hoc requests. Instead, they proactively propose solutions to business problems using data. To effectively do this, you need to get comfortable with detailed product analytics and developing an understanding of how your solution will be delivered to your users." - Sebastian Perez Saaibi, Senior Data Science Manager
Full stack data scientists are generalists versus specialists. As full stack data scientists own projects from end-to-end, they work with multiple stakeholders and teams, developing a wide range of both technical and business skills, including:
- Business acumen: Full stack data scientists need to be able to identify business problems, and then ask the right questions in order to build the right solution.
- Communication: Good communication—or data storytelling—is a crucial skill for a full stack data scientist who typically helps influence decisions. You need to be able to effectively communicate your findings in a way that your stakeholders will understand and implement.
- Programming: Efficient programming skills in a language like Python and SQL are essential for shipping your code to production.
- Data analysis and exploration: Exploratory data analysis skills are a critical tool for every full stack data scientist, and the results help answer important business questions.
- Data engineering: A full stack data scientist should have the engineering skills to build and deploy model pipelines to production.
- Machine learning: Machine learning is one of many tools a full stack data scientist can use to answer a business question or solve a problem, though it shouldn’t be the default. At Shopify, we’re proponents of starting simple, then iterating with complexity.
What’s the Benefit of Being a Full Stack Data Scientist?
“You get to choose how you want to solve different problems. We don't have one way of doing things because it really depends on what the problem you’re solving for is. This can even include deciding which tooling to use.”- Yizhar (Izzy) toren, Senior Data Scientist
“You get maximum exposure to various parts of the tech stack, develop a confidence in collaborating with other crafts, and become astute in driving decision-making through actionable insights.” - Siphu Langeni, Data Scientist
As a generalist, is a full stack data scientist a “master of none”? While full stack data scientists are expected to have a breadth of experience across the data science specialty, each will also bring additional expertise in a specific area. At Shopify, we encourage T-shaped development. Emphasizing this type of development not only enables our data scientists to hone skills they excel at, but it also empowers us to work broadly as a team, leveraging the depth of individuals to solve complex challenges that require multiple skill sets.
What Tips Do You Have for Someone Looking to Become a Full Stack Data Scientist?
“Full stack data science might be intimidating, especially for folks coming from academic backgrounds. If you've spent a career researching and focusing on building probabilistic programming models, you might be hesitant to go to different parts of the stack. My advice to folks taking the leap is to treat it as a new problem domain. You've already mastered one (or multiple) specialized skills, so look at embracing the breadth of full stack data science as a challenge in itself.” - Sebastian Perez Saaibi, Senior Data Science Manager
“Ask lots of questions and invest effort into gathering context that could save you time on the backend. And commit to honing your technical skills; you gain trust in others when you know your stuff!” - Siphu Langeni, Data Scientist
To sum it up, a full stack data scientist is a data scientist who:
- Focuses on solving business problems
- Is an owner that’s invested in an end-to-end solution, from identifying the business problem to shipping the solution to production
- Develops a breadth of skills that cover the full stack of data science, while building out T-shaped skills
- Knows which tool and technique to use, and when
If you’re interested in tackling challenges as a full stack data scientist, check out Shopify’s career page.
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