Everyone should be a data scientist. I don’t mean the profession—although there are lots of opportunities if that’s what you want to do. What I am referring to is a mindset and a set of actions for career planning, development, and advancement. To find your career bliss and to advance in your profession, you need to constantly collect and analyze data—about yourself, what brings you joy, and the professional ecosystems where you thrive. Becoming a data scientist is your indispensable insurance against a career mismatch.
This is not an easy task. The system you are measuring—you—is biased, dynamic, and complex. To further complicate matters, you are both the experiment and experimenter. Your data collection never ends and often occurs in real time, potentially influenced by outside sources. If you are not careful, these external stimuli can make you forget who you really are.
I was reminded of this last summer while on holiday in Marseille, France, on a bike tour along the coast that was to culminate with a dip in the Mediterranean Sea. After a few hours of pedaling in the heat, I could hardly wait to jump in. But when we got to the small beach, I was dismayed to find that there was nowhere to change into my bathing suit. “I guess I won’t be swimming,” I thought. I plopped down on the sand and glumly watched everyone else splash about.
But as I listened to the birds and the wind and water and the giddy squeals of nearby petits enfants, I realized something: The external stimuli of worrying about what other people would think was pushing me toward making a decision that would not be the right one for me.
“I am standing in FRANCE, at the edge of the freaking Mediterranean Sea, and I am not going to go in because I’m not wearing the ‘requisite’ outfit???” I cursed at myself. “I must be nuts!” I declared. Then I emptied out my pockets and splashed into the water in my bike clothes.
I went into the sea because I remembered I’m me.
This is your two-part career task as well: One, know who you are and what brings you joy. Two, look for professions, jobs, and ecosystems where you can be your authentic self and pursue joyous, prosperous employment.
So let’s get started with the first part, data collection. (We will address the second part in future columns.) The following approach is a method I have used for years. It is just one of the many ways to collect data about yourself, but I’ve found that it’s a great way to get started.
First, take out a sheet of paper and divide it into six columns. In the first column, list every experience you have had—literally anything that gave you experience solving problems. These could include jobs, volunteer gigs, teaching, your dissertation work, your postdoc, a short-term project, a class assignment, a leadership role, a community activity, and more. You worked at a fast food restaurant when you were 18 and quit after 2 weeks when you realized what goes in the “special sauce”? List that experience, too. Then, for each experience, you’ll work across to complete the five remaining cells in the row: problems solved, skills gained, characteristics learned, what you loved, and what you hated.
For problems solved, think about what you accomplished and the tasks you completed as part of each experience. Include the technical scientific problem-solving you did as part of your research as well as nontechnical problems you’ve tackled, both as part of your research or through other experiences. For example, perhaps as head of the postdoc affairs committee, you helped organize a professional development conference for fellow postdocs. The problems that you solved included leading and organizing the team, negotiating for the space and food, developing marketing materials to encourage people to attend, giving the welcoming speech, and raising funds.
For this and the rest of the columns, don’t get hung up on finding the right vocabulary or crafting perfect sentences. A stream of consciousness approach and simple words and phrases can suffice.
The problems solved are closely tied to the next column, skills gained. Using the same example of executing the postdoc conference, you developed skills in event planning, public speaking, team building, project management, accounting, fundraising, and marketing. Again, this section should include both technical skills—for example, mastery of an experimental technique or data analysis program—and soft skills, such as communications, negotiations, and conflict resolution.
For the next column, think about what characteristics you realized about yourself from that experience. This column will require deeper introspection because you are building a bridge between the facts—your solutions and skills—and your feelings about those facts, which come in the next two columns. One approach is to think about your mode of solving the problems you faced. For example, through my undergraduate astrophysics research, I realized that I work well independently and that I learn best when things are verbally explained to me. Maybe you’ve had experiences that taught you that you are a good leader or that you work well under deadlines. Identifying these types of personal attributes can be tricky—they may feel hard to define or to put your finger on—but it provides an important foundation for your career exploration journey.
Finally, in the last two sections, articulate what you loved, loved, loved—and hated—about each experience. Be as granular as you can. Perhaps you loved working with X equipment, or thinking about Y subfield, or collaborating with someone with a Z personality. Maybe you enjoyed looking out the window at a tree—or a cactus. Maybe you loved being able to contribute to the betterment of humanity. Maybe you hated programming in Python, using an atomic force microscope, or not being able to travel as part of your job. Whatever it is, note it. This is the data that will help you more clearly understand yourself, articulate your needs, and design the career of your choosing.
Once you have your matrix reasonably completed, the next step is analyzing the data to draw conclusions and determine some avenues for further investigation. Look for two things: patterns and spikes. The patterns are repeated words and phrases in the column where you listed what you love. These repeated moments of joy could point the way to what your unicorn career could encompass. The spikes are places in the matrix where you have written a lot. This generally indicates that you felt energized, creative, productive, and happy when doing whatever it is you were writing about—that’s why you’ve written so much! These spikes help point the way to your true self.
This may sound like a lot of work, but I assure you that it will be effort well invested. Make it a habit to continue this data collection on a regular basis and you will begin to have clearer ideas about which career rue would be right for you. The data will also serve you well in your future career planning, helping you write customized CVs, resumes, and cover letters and prepare for interviews.
Returning to my own voyage of data science and self-discovery in the Sea: During my frolicking, I scraped the top of my feet on the rough rocks at the bottom of the basin. Today, months later, it still looks like my feet were grabbed by an especially enthusiastic gryphon. I now have a visual record of my achievement—the achievement of being me and not letting the outside world define who I am. And indeed, that is always the goal.
*Concepts from this column come from and build on the author’s previous published works, including articles, speeches, and her book titled Networking for Nerds.