It may seem antithetical that the step toward becoming a great communicator might be to say nothing at all.
But in groups and teams, effective communication isn’t just about getting your two cents in — it’s also about listening to those around you.
“To listen entails a fundamental letting go of self-centeredness,” writes Oren Jay Sofer in his 2018 book, “Say What You Mean: A Mindful Approach to Nonviolent Communication.” “We have to be willing to put down our own thoughts, views, and feelings temporarily to truly listen.”
As the cross-functional work model becomes more and more mainstream, communication — starting with listening — may be the most fundamental part of an effective workplace. For instance, how a customer success manager talks (and listens) to a sales lead is different from how they would with a data scientist.
But, as Foursquare’s Senior Data Scientist Aileen Li recently shared with Built In SF, “Being able to tailor your communication style to your audience to convey complex results effectively is a skill that truly makes you stand out.”
In the field of data science, quantifiable hard skills help you land the job. But soft skills, like effective communication, can lead to the improvement of other skills, like empathy and perspective, that help you thrive once you’re there.
For more on what skills data scientists should be learning to improve in their jobs — and exercises on how they might get there — Li shared more about what non-technical skills she sees as being important for emerging and established data scientists alike.
What have been the most important non-technical skills in your career thus far, and why?
Communication, organization and my ability to question my results. Data scientists need to work collaboratively with a team of technical and non-technical stakeholders. Being able to tailor your communication style to your audience to convey complex results effectively is a skill that truly makes you stand out.
Being able to break down your project into manageable chunks that can be prioritized and resolved systematically helps you stay on track with the project timeline. Understanding that data science is often an iterative process, having a plan also helps in recognizing when to stop iterating when the business requirements have been met.
It’s really helpful to take a step back from the technical details of your models and ask yourself if the results are reasonable, and whether they truly answer the business question that you’re solving. It is also useful to anticipate questions that could be asked by a manager or a new team member on the project, and see if your results could answer those effectively.
Set aside time to regularly keep up with the news and novel industry techniques, and use that as inspiration for small side projects.
Beyond building and maintaining robust analytics and coding skills, how would you advise a talented data scientist who wants to grow their career?
One experience I would recommend to data scientists who want to develop their communication skills is to participate in knowledge-sharing opportunities at your company or outside the organization. This could include simply giving a short talk about what you’re working on for your team, a longer presentation to a larger interested audience, or even participating in a conference.
Another valuable practice that helps to contextualize and add perspective to your problem is to ask for feedback from your peers, manager and other domain experts.
Lastly, be curious! Set aside time to regularly keep up with the news and novel industry techniques, and use that as inspiration for small side projects.
