Dr. Scott Olesen was one of the founding cohort of Biological Engineering Communication Fellows, who joined when the Lab was established in 2013. He was a BE PhD student in Prof. Eric Alm’s lab and developed quantitative and statistical methods for microbial ecology and clinical trial design. Scott graduated with his PhD in 2016 and then completed a postdoc at the Harvard Chan School of Public Health, studying the relationships between antibiotic consumption and antibiotic resistance.
What are you doing now?
I am Scientific Director for OpenBiome, a medical nonprofit based in Cambridge/Somerville. I help shape the organization’s scientific goals, organize operations, and provide broad scientific and personnel leadership.
How are you using communication skills in your job?
Insofar as I’m a scientist doing scientific work, it’s the same things as a grad student: explain concepts to colleagues, write papers, read papers, review papers, etc.
Insofar as I’m a member of unstructured teams working on unstructured problems, I spend a lot of time in meetings, presenting ideas to various people.
How did the Comm Lab help prepare you for this role?
The things that helped me become a better science communicator are the same skills I use in everyday communication at work – which is more important than the science communication that I used to do! In a grad student science talk, if you present a topic badly, people might be confused or bored. At my job, if I present things badly, then people get confused and spend a lot of time doing useless work because they are confused, or they make bad decisions because I haven’t educated them properly.
What are some communication strategies that you can share with current trainees?
1. Know where to start.
In science communication, I think about this as the hourglass metaphor, or the fact-chaining metaphor: the first thing you say sets you up for the second, then the third, and so on, until your listener is prepared to digest your main point.
As a grad student, I did this for every talk, asking myself, “Do I start with, ‘Cancer is a problem’, or with ‘JAK-STAT 2 is a dynamic system’?” Now, as a job person, I do this in every single conversation. As a grad student, everyone is doing science all day, so you can jump in with, “Hey, did you know that STAT phosphorylation is linked to so-and-so?” As a job person, you have no idea what anyone remembers about whatever you’re about to talk about, so picking a strategic starting place is critical.
2. All communication is education.
Bad education is giving someone a bucket of facts. Good education is giving someone a system so they can come to conclusions. As a grad student, I would say, “This is the scientific question. Here’s why existing knowledge/tools/approaches aren’t enough to answer it. So what do you do?” In that moment of aporia, you provide the system – an interconnected set of facts that allows people to come to conclusions. Rather than saying, “Freeze/thaw cycles are bad for sequencing samples,” you can say, “Sequencing requires intact DNA. DNA is most likely to be intact when cells are whole. Freezing breaks up cells.” Then someone can draw other conclusions (e.g., vigorously mechanically agitating samples is probably also bad for sequencing.)
As a job person, I don’t use scientific systems so much as principles – what do we as individuals or as a company value? “Are we the kind of company that does X or Y? If X, then we take action Z.”
In general: ask what you want your audience to be capable of doing after you finish communicating, not what you “want them to know.”
3. Compartmentalize the details.
This is closely linked with 2. As a grad student, my PI would want to dive into one data point that didn’t look like how he thought it should. I’d say, “OK, let’s set that aside, because it won’t change the larger picture right now.” The funny part is that, as a grad student, you have nearly infinite time. As a job person, time is much tighter. So forget talking about a data point, you need to know if you’re going to have the time to talk about an entire data set, or an entire way of approaching a problem. Yesterday I was in a conversation where 5 people wanted to dive into 5 different sets of details. I tried to ask, “OK, if we dove in there, what kinds of things will we learn, or be capable of doing?” Diving into details can sometimes lead to fascinating scientific surprises, but usually it leads to lost time.
Learn more about Communication Fellow alumni here.