Data Scientists: Learn to “Read the Room”
When someone shows you who they are, believe them the first time. — Maya Angelou
You reach out your hand, grasp the door handle, turn it and push open the door. As it gently swings open and comes to a rest, you see 3 people: two are laughing and talking with each other and one is staring at their phone with an angry look. Does this quick information tell you anything about how to communicate with the people in this room? Obviously, after you learn more about them you will have a better idea on how to approach them. This ability to understand the mood in the room and how receptive the people are is the ability to “read the room.” Knowing your audience and reading a room are very close topics, but knowing your audience pertains more to when you craft a message prior to delivering it, whereas reading the room also prepares you for working together and networking.
Working as a Data Scientist, you either are or will be in a unique situation simply due to your title. The expectations for obtaining value from all-things-data have been placed on you in some respect. Whatever room you walk into (virtually or otherwise) will have people whose experience with Data science ranges from 0 to practitioner, and their expectations of you can have just as much variation. Being able to quickly ascertain which people in the room will be most receptive and easiest to work with lets you optimize your time spent and change your tactics for members of the room that negatively impact the mood.
Reading a room doesn’t require mind reading, just observe, put yourself out there and listen.
Observe
What’s the mood like? Does small talk get started easily and carry on into a meeting, or is it shut down? How early and late do people show up to the room or meeting? In the first 5 minutes of entering a room you can get a sense of how intense discussion is going to get.
If you work at a large company, you can do some homework before entering the room. Take a look at where people sit within the organization (director, VP, etc) and what they do within the organization (IT, finance, etc). Use an organization chart. If you work at a small company, ask around, starting with your team.
Look for body language that indicates boredom or antipathy. Usually, a meeting including Data Scientists will take some level of engagement and concentration, so a lack of interest in the room signals to you that it will be a difficult conversation. My advice for a room that contains some people with a lack of interest is to concentrate my focus on the remaining people until they all show interest.
I had a meeting start once with a co-worker jokingly asking “why am I even here?” (referring to themselves). I could have let that statement diminish the importance of the meeting by acknowledging it, but instead I introduced some quick insights focused at the rest of the room, who seemed receptive. By the end of the meeting, the entire room (including the rude co-worker) was engaged in the discussion.
Put Yourself Out There
The basic idea of “putting yourself out there” is to show a little vulnerability and get some direct interaction with the room.
Be more than approachable about your role at the company or in the project. That means speak up either about work you have done or what you plan to do and any questions. I always suggest leading with a naive question, something that would be clear to a subject matter expert. The reaction is always telling: you may get a straight answer, a detailed explanation or reluctant answer. If there is an issue with basic questions in the room then that might indicate a reluctance to re-examine basic assumptions. Another thing you can try is stating that most AI projects fail. A confused look might indicate a lack of experience in the space, whereas another person might offer suggestions why they think that is the case. In that scenario, be prepared to share your thoughts. The point isn’t to say whether or not AI projects fail, its to see how the room feels about a newer concept (AI) and get an idea of how they feel about Data Science in general.
Listen
Listening is a key skill that we all have to some degree. I struggle to do this well because being a good listener sometimes means listening without thinking of something to say or how it relates to what you are thinking. Listening well is especially important in the case of virtual meetings where things like body language and eye contact won’t be present.
If you really want to the read the room, listen to how long people are given to speak. If speakers are talking over each other frequently, you might not have time to convey a lengthy explanation. Especially in data science, when there are concepts that are difficult to explain verbally, just state that you’ll come with a visual aid next time if the room seems impatient.
Another thing to listen for is length of disagreements. Are disagreements present at all? If so, are they resolved quickly? Are they resolved to appease certain people? If you are working as a Data Scientist, you’ll likely get asked some very pointed questions and you’ll likely disagree with somebody at some point. If you detect that the room tries to move past disagreements quickly, that could mean that they are never really solved. I always make it a point to say that the presence of a disagreement points to the subject’s importance, and that it can always be revisited.
Summary
Reading the room is all about quickly figuring out what the mood of the room is and how people will behave. This can be applied to individual meetings where you are the subject of interest, much like knowing your audience. This can also be applied to workshops or projects where you will spend more time with your new coworkers. Take a step back and let people tell you who they are and, if you are feeling bold, be a little vulnerable put yourself out there to invite people to interact with you directly.
