Guest blog by Justin B. Dickerson, PhD, MBA, PStat, Chief Data Scientist at Snap Advances.
Okay, that headline was meant to get your attention. But lately, I’ve been thinking about this crazy circus we call data science and how everyone seems to think data scientists are invaluable, treasured, and potentially “un-fireable” in this age of data scientist negative unemployment. There are far too many comments on data science blogs talking about the best Kagglers and the latest approaches in “deep learning” and other technical parts of the discipline. And there are precious few posts about the reality of being a data scientist who needs to function with real human beings in an organization on a daily basis. So, I decided to step back and think of 5 pitfalls a data scientist could encounter which could lead to unemployment (yes, even really smart people get fired).
So, here we go:
- You believe the workplace is your own personal university. I’m particularly fond of reading posts from one of my peers on esoteric “ideas” from the data science elite (the “top” data scientists) when I know he works for a company that would never have any need for such ideas. Personal growth and training is vital. However, you were hired to increase shareholder value, not to become a modern-day Plato. Kaggle on your own time.
- You are always the first to speak in a meeting and must have the last word. I remember a friend relaying a story to me about being obnoxious in law school. As best I remember, the saying goes, “if by the second week of class you don’t know who the jerk is, it’s you!” Slow down your speech. Take a breath. When you speak, have something meaningful to say in 10 seconds or less. Speak slowly, deliberately, and thoughtfully. Recognize the points of view held by others in the meeting. Understand the difference between communicating and talking. You have to remember you have a target on your back at most meetings. People know you’re intelligent. They just want to see if you’re also arrogant. Provide them with a pleasant surprise using the tactics mentioned above.
- You’re a novelist, not a sound-bite writer. A former boss of mine became so frustrated with my lengthy and technical emails that he forbade me from writing emails more than 4 sentences long. It seemed impossible at the time. But looking back, it became clear that few people read the verbose explanations. Executives are looking for the sound-bite or as I’ve heard it called, the WIIFM (What’s in it for me?) You don’t get paid to write novels. You get paid to produce headlines.
- You’re seen as just plain lazy. Intelligent people have a propensity to only want to work on projects that satisfy their need for intellectual curiosity. This is not necessarily a bad thing. However, you’re part of a team. Every team has “junk” work. In data science, it’s generally hours of “data wrangling.” Don’t dump stuff off on other team members. Take on the project no one else wants to do. Show your team that you’re willing to put down the intellectual mantle and simply grind it out with them.
- YOU FORGET A UNIVERSAL PRINCIPLE………..THE HIGHER THE SALARY, THE BIGGER THE TARGET. If you think for a minute that you won’t be fired because you’re irreplaceable, highly-skilled, and highly-paid, let me introduce you to the real world. If you have a graduate degree and decent experience and technical skills, you’re generally making a six-figure income as a data scientist. It’s very easy to lose sight of the fact most of the people in your organization are paid less than you are paid. In some cases, much less. I leave work each day asking myself one question, “did I pay for myself today?” You better believe your boss is asking the same question. If you can’t show your value, eventually the math just doesn’t add up for the boss.
I realize these are high-level generalizations. But I grow tired of reading nothing in the data science community other than technical articles. You will be a much stronger data scientist and leader if you remember some of the ideas above. Good luck!
Originally posted here.
About the Author:
Justin is a professionally accredited (American Statistical Association) and academically trained statistician/data scientist with primary doctoral training from a Tier 1 research university in advanced statistical methods, research design, and economic analysis. He has deep expertise in predictive modeling across a variety of industries. He also has substantive experience with big data platforms and strategies, including machine learning algorithms. Justin has additional training and experience in corporate finance, forecasting and analysis. He also managed more than 30 employees and led several departments in Fortune 500 companies.
