Basque, spoken in Spain and France, as well as Burushaski, which is spoken in northern Pakistan, are both language isolates, or languages that don’t belong to any known family. There is no one theory as to how these languages developed, but two hypotheses include isolation owing to rugged terrain and the death of the larger language families over time.
If the tech industry has a language isolate, then it’s the language spoken by data scientists. Data scientists work at the intersection of statistics and technology and speak a language very few other fields possess full fluency in, which is why just about every data science job posting calls for strong communication skills.
Data scientists with strong communication skills do more than just make the results and recommendations from their experiments easy to understand. They are also more proactive. This has been the experience of Yitong Chen, a product data scientist at Cash App, who has invested herself in becoming a better communicator with — and more understanding of — her colleagues on the product management team.
“I listen to what the product team cares about and what drives product decisions,” Chen said. “The more I communicate cross-functionally, the easier it is to build my own product sense. Instead of waiting for data tasks to be assigned to me, I am able to think like a product manager and initiate new data science projects that bring value to the products we’re building.”
Originally designed as a peer-to-peer payments app, Cash App is now a fully fledged financial platform that offers banking and investment services.
How does frequent communication with cross-functional stakeholders — especially non-technical ones — benefit your team?
Effective thought partnership with product managers allows me to better understand the hypotheses and reasoning behind the data. My motivation is driven by curiosity: How can we use metrics and trends to identify product opportunities? How do the results of an A/B test inform product decisions? The more I communicate cross-functionally, the easier it is to build my own product sense. Instead of waiting for data tasks to be assigned to me, I am able to think like a product manager and initiate new data science projects that bring value to the products we’re building.
When it comes to updating non-technical stakeholders on your team’s latest efforts, how do you translate the complexities of your work into digestible language?
I like to build empathy with my audience. While something might seem straightforward to me, it may not to others. Every time I write up a data summary, I think about how I can use the simplest words to describe my findings and make them easy to digest. I listen to what the product team cares about and what drives product decisions. I enjoy reading weekly updates and business decks from stakeholders so that I can discover more about their thought process and speak the same language as them.
In addition, I am always seeking constructive feedback from my stakeholders, peers and leaders and asking them if my data story makes sense and how I can hone my presentation skills. Feedback doesn’t equal criticism, and data science is a two-way street. Our technical work is translating business questions into statistical formulas and codes while communicating with stakeholders is the other way around — we are translating numbers back into business solutions.
Feedback doesn’t equal criticism, and data science is a two-way street.’’
When you needed to get buy-in for a data science initiative from a non-technical stakeholder?
I was browsing the Cash App Card customization page, checking out all the ways cards can be customized, from emojis to original designs. While designing my own card, I wondered how the experience impacted the whole card order flow and what may lead to drop off. I decided to run a thorough user journey analysis on card orders and spoke with stakeholders about how this analysis could resonate with our product goal. This is the first step of user engagement on Cash App Cards — we have 20 million monthly active card users as of March 2023 — and understanding user pain points is critical to improving the entire product life cycle.
To secure this buy-in, I was fully transparent about my capacity to make sure I wouldn’t delay other high-priority projects. This analysis turned out to be successful and gained visibility from multiple product teams. We improved the customer experience by enhancing different features for first-time card users and existing customers who needed a new card. What I learned from this experience is to marry analysis with product goals, keep transparency on priorities and tie the data back to actionable insights.
