Skip to main content

Interview with an Intuit Principal Data Scientist 

At Intuit, our data-driven work model drives innovation for impact across customer solutions.  When data scientists have insights into how customers use our products, we are able to develop strong insights into which features matter most in our customers’ financial lives. 

For example, last year a feature was developed giving customers the option to receive their refunds up to five days earlier.  Experimental results confirmed strong customer demand that led to increased entry points to the feature driving awareness and higher utilization. 

Dive deeper: Here are more details.

Introduction of Alex Zhou, Principal Data Scientist

Alex Zhou, Principal Data Scientist has been with Intuit for 2 years.  In the limited time that he has been with the company he accomplished high profile projects including his story below.  

One of the most exciting projects I worked on recently was building an algorithm to determine where to deploy TurboTax’s physical stores and offices. This work supported a nationwide launch of nearly 600 Expert Office locations and 20 brand-new TurboTax Stores. My model identified the highest-potential ZIP codes to target, helping us reach and unlock as many customers as possible.

These stores represent a clear departure from traditional tax offices. They’re designed around a seamless blend of digital and in-person experiences, allowing customers to start their taxes in one place and finish in another—whether they begin on their own device or with the help of a TurboTax expert. Here is Sasan, our CEO, sharing more details about the public launch.

What skillsets do you need for this role at Intuit? 

  • Programming skills:  to turn messy, large-scale customer data into reliable, repeatable pipelines and models.  This allows data scientists to scale what would have been a subjective decision into a data-driven one.
  • Statistical knowledge and machine learning:  Statistics and machine learning help move beyond correlation to make better decisions. It requires experimentation, causal reasoning, and predictive modeling to remove bias and optimize for specific outcomes. 
  • Data visualization:  make complex findings intuitive and actionable for partners. Whether it’s showing geographic opportunity, experiment results, or customer funnel behavior.  The goal is to clearly communicate why a solution matters and where it will have the most impact—so decisions can be made quickly and confidently.
  • Business acumen and storytelling:  We are big on  slide decks and presentations to leadership and other cross-functional stakeholders. Right framing, providing the right context, and connecting the dots between insights and business action is critical to the progress and understanding of any project being developed.  Unlike statistical knowledge and data visualization, storytelling begs the question “so what”—how a feature improves a customer’s financial life or how a model unlocks growth.
  • Collaboration and communication:  Our data teams work consistently with product managers, engineers, designers, and leadership to ensure data solutions are practical and customer-centric.

I view data science as a team sport—listening to context, translating technical results into plain language, and iterating quickly so insights can turn into real customer impact.

Alex ZhouPrincipal Data Scientist

One testament of Intuit’s culture is the openness to learn, mentor and be mentored, establishing a strong foundation of trust, knowledge, and practical skills.  

Learn more about our different career pathway opportunities here.  

How do Intuit’s data scientists differ from data analysts?  

Data scientists and data analysts share a common goal—using data to solve business problems—but they often approach those problems with different toolkits. Data scientists typically leverage more advanced statistical modeling, causal techniques, and machine learning to reduce bias, build automation, and deploy models that optimize specific objective functions. 

Data analysts, on the other hand, tend to focus more on descriptive analysis, reporting, and generating insights that inform decision-making. 

While the methods may differ, the end goal is the same: helping the company make better, data-driven decisions. The distinction is less about what problem we’re solving and more about how we choose to solve it.  Intuit is customer-obsessed and our approach to innovation stems from a belief that we are proactive in experimentation to drive innovation for our customers worldwide.

Dive deeper:  becoming a data scientist. 

Gain experience with Intuit 

Practical experience helps bridge the gap between learning and doing.  Diving into online classes or courses really provide entry-level professionals applied experiences to hone their skillset and boost resumes. 

Our award-winning internships and co-ops are geared towards collegiates actively studying in tech fields to join us and work on real-world projects to contribute to customer prosperity. What better way to leverage your contributions across your CVs and resumes.  

Of course, the road to data science is not always linear.  Some individuals may switch careers.  Intuit’s Career Pathways Programs provides budding professionals with the opportunity to accelerate their career through an “earn while you learn” model.  

A strong portfolio often speaks louder than a resume. Contribute to open-source projects, write about your work, or showcase your skills through data storytelling and dashboards. Real-world examples speak volumes. If you don’t have work to show off yet, you can find portfolio projects online that will help you apply (and showcase) your skills.  

Get connected with our data science jobs at Intuit to stay in the loop for new opportunities or check out our open data science roles.