As researchers, collaboration is the air we breathe. And some of the most impactful projects I’ve worked on were often in close collaboration with an amazing data scientist.
Data scientists primarily focus on the what and how many to identify trends, correlations, and anomalies. They work with quantitative behavioral data like performance metrics, conversion funnels, and statistical models to identify patterns in behaviors that are happening at scale. While this often offers statistical validity, it can sometimes miss context or underlying motivations of why something is happening.
That’s where UX Researchers come in, exploring the why behind user needs, behaviors, and motivations often through qualitative methods like user interviews, surveys, observations, usability tests, and more.
Together, these roles are two faces of the same coin. And when they work together, they not only give organizations a more holistic perspective of the user experience, but their work often ends up having more business impact.
I know firsthand how valuable analytics can be for a company; I also know of its limitations. There are times when you have to dig deeper than possible with a query or a regression model. And often the best way to do this is to simply talk to the people you want to use your product. A truly data-driven organization will complement their analytics with user research and use learnings from one to power the other. - Utsav Kausish, Former VP Analytics, User Interviews.
“I think there is still a preference for quantitative data over qualitative,” says Morgan Koufos, Lead UXR at User Interviews. “To help UX research gain more buy-in and trust from stakeholders, bringing in data analysts to support the quantitative side can help you meet people where they are—especially if they value hard data.”
In this article, I’ll explore how these roles naturally complement each other and share what it takes to build an impactful partnership.
Research logistics shouldn’t eat your time. User Interviews handles recruitment and management, so you can focus on the strategic work that drives impact, like establishing cross-functional relationships.
Book a demo today.
How to start a UX research x data science collaboration
According to Maria Kamynina, a UXR turned data analyst, both research and science roles are incredibly similar. They both include working with large datasets to uncover insights, answering complex questions, continuously learning, and challenging assumptions.
Despite the similarities, each discipline has its own approach, language, and goals that need to be mutually understood before the work begins. Effective collaborative research requires setting agreements upfront, which means you must chat with stakeholders about how you plan to work together.
Here’s what I’ve found has worked during this process:
Align on language: Terms like “activation,” “significant,” and “insight” can mean different things across disciplines. Take time to establish common definitions upfront to reduce misunderstandings down the road. This might take the form of a shared insights glossary that is designed by both UXRs and data scientists.
🗣️ Tip: Use linguistic mirroring as a shortcut in early convos to align on language.
Recognize different forms of validity: UXRs value depth of the customer experience, while data scientists prioritize statistical significance. It’s important to keep in mind that neither is better—rather, both types of data complement each other.
Respect each other's timelines: Both roles operate on different cadences. Research projects often have longer timelines, and it takes time to see the impact of the work. In contrast, in user analytics, a small analysis can sometimes drive impactful decisions quickly. Be sure to build in extra time for collaboration or consider phasing your studies. During my time at User Interviews, this often looked like the data team starting the analysis first, followed by a UXR conducting a qualitative deep dive, depending on the subject matter.
What UX researchers and data scientists should collaborate on
Collaboration between data science and user research typically happens at key moments throughout the product lifecycle:
Product Discovery: While UXRs conduct discovery or generative research to uncover needs, data scientists can explore behavioral data to identify drop-offs and understand usage patterns. Together, this data can be used to prioritize different opportunities on the product roadmap.
Feature experimentation: A/B testing is a method often used to run experiments and determine how different versions of your product performs. When designing these tests, UXRs can help frame hypotheses and identify strategic metrics based on customer needs. During testing, UXRs can help dig deeper into unexpected behaviors. After testing, data scientists can conduct a deep quantitative analysis to understand the findings at scale.
User segmentation: Data scientists excel at identifying different user groups based on behavioral patterns, while UXRs can develop rich personas that uncover user motivations and contexts. During my time at User Interviews, we ran an intensive customer segmentation study to learn more about purchasing behaviors around Research Hub. Data science investigated purchasing behaviors, and then the research team led interviews to uncover mindsets around purchasing software.
Making the most of your UX research x data science partnership
Once the wheels start turning on this partnership, it becomes incredibly powerful. The more reps you get in together, the more strategic and proactive the collaboration can become. This requires:
Leading with questions, not methods: It may be tempting to start with interviews or A/B tests, but it’s most effective to begin with the problem you're trying to solve. Koufos recommends meeting up early to discuss questions you're investigating, dividing them between data and research, and setting up a weekly touchpoint to share updates along the way.
Using data to validate qualitative research: As a UXR, you might hear about an issue mentioned by a few users. A data scientist can quickly check how widespread the issue is and how many customers may have experienced the same friction points. Triangulating data helps the organization build confidence in your findings and support prioritizing the issue on the roadmap.
Don’t be afraid to triangulate data. There [are] so many ways to do it, and so many data sources [at your disposal]. The companies that succeed are the ones comfortable triangulating data and doing the hard work—because that's how uncover the insights that other people can’t find. - Claudia Natasia, CEO, Riley AI
Establish shared rituals: Don’t do your work in silos. Schedule knowledge-sharing sessions where teams can present readouts of their findings. Share access to dashboards, analytics, and research repositories. This transparency will allow teams to build on each other's knowledge and deliver greater value together.
📊 Tip: Start learning and using user analytics tools like Mixpanel, Amplitude, and FullStory to better understand user behavior at scale and the metrics your team relies on. Ask for access to existing dashboards and create custom views based on patterns you’ve observed in your own research. This will help you build a strong baseline. If possible, schedule time with a data scientist to walk through how these metrics connect to key performance indicators (KPIs). Read our Product x Research Collaboration Report for more!
When Dr. Danielle Smith was the Director of Experience Research and Accessibility at Express Scripts, she partnered with the data science team to build an internal participant panel, enabling the user research team to recruit specific participants on their own.
User Interview’s Research Hub CRM helps organizations build a data-rich panel for use across multiple teams, with powerful segmentation and governance tools. Book a demo to learn more.
Finding success: what teams get right
When you begin to integrate data and research, you may notice some major mindset shifts occurring across your team.
You might see different functions becoming genuinely curious about other disciplines. I’m not talking about the “fake it ‘til you make it” kind—I mean a real interest in how other individuals and teams tackle problems. This could look like attending presentations, asking questions about how work was done, and seeking more opportunities to collaborate.
You may also want to consider teams sharing related goals. With both user research and data science contributing to product outcomes, it might make sense to have shared key metrics rather than discipline-specific ones. This enables collaboration to become more fluid. Regardless, executive leadership must lead the way and reward individuals and teams when data and research work in tandem toward a shared goal.
Some organizations famously shifted their structure to better enable this collaborative work. Spotify and Dropbox, for example, both designed unified insights functions aimed at having data, research, and insights teams work together to solve the business's most important questions.
Final thoughts: quant + qual = 💗
The best collaboration I’ve seen and experienced between UX researchers and data science is one grounded in mutual respect, curiosity, and a willingness to sit in the trenches together. You just need to start with openness and a commitment to navigating things together.
If you're a UXR wondering how to deepen your partnership, start small with a single project where mixing research methods could bring better insights. Reach out, share context, ask questions, and share your impact. You can use this as a case study to define new ways of working across your team in the future.
If you're a data scientist, get curious about qualitative research—sit in on interviews, and you might be surprised about what you learn.
When these two functions move in sync, the entire organization benefits. Data becomes more human, and insights more actionable.
More resources
.png)
