People tend to make terrible career choices. In fact, over 70% of today’s workforce is unhappy with their jobs. We wind up in careers we dislike because we are forced to make career decisions when we’re too young to know enough about ourselves and the vision of the world we want to be in.
One way to fix this problem is to formulate a coherent conceptual framework for making career decisions. What is the right way to pick a career? What points should be factored into this decision? What do I need to know about the world and how it’s changing? What should I assume about my future self’s preferences?
The framework I offer here is inspired by strategies in artificial intelligence that have worked incredibly well to solve problems in computer gameplay (e.g., chess and go) as well as robot motion. Specifically, a great strategy is one that maximizes the set of future options. Applied to career decisions, we must flip the problem on its head: instead of asking “how do I pick a career path,” we should ask “how can I maximize my future self’s options so that I am not forced to pick only one path?”
A career in data science satisfies this option-maximizing criterion. It’s not the only good way to think about career choices, and data science isn’t the only answer. It is, however, a great way to start thinking critically about what is probably the second most important decision in your life (after whom you marry!).
So, how does data science maximize your future self’s options? As a field that is gaining significance across industries, data science isn’t going anywhere any time soon. Savvy data scientists are professionals with the training and curiosity to make discoveries and informed, quantitative decisions in the world of big data.
As a field, data science rose to prominence owing to record levels of raw material: structured and unstructured data. A couple of secular forces gave rise to this career, including the decreased cost of data storage and increased accessibility through cloud networks. The number of sensors is increasing (satellites, cameras, phones, internet of things and so on). Computing power is increasing. Insight-extraction algorithms (e.g., machine-learning techniques) are becoming more sophisticated. All of these forces are going in one direction only, making it very likely that the skill of wrangling data to produce actionable insight will be rewarded for decades to come. Recognizing this situation, Wharton recently announced the introduction of data science into its MBA curriculum.
Learning data science forces you to learn useful auxiliary skills. Building the technical attributes to apply yourself is top line. There’s something unique about being able to predict scenarios or identify trends from amorphous bits of data. But one must have the right combination of data visualization, analytics, predictive modeling, supreme organization and communication.
Another valuable skill is coding (Python and R, at a minimum) to further strengthen your data-science skills. Coding is valuable and expands your future options; it also makes you think logically and in terms of algorithms. In addition, data scientists are adept in learning to manage unusable or irrelevant data.
The other obvious skills you’re required to learn are probability, statistics and deep contextual understanding of any given situation, all of which are incredibly useful for forecasting under uncertainty.
Finally, and perhaps most importantly, learning data science requires you to learn how to communicate and how to tell a compelling story on the basis of the data you see.
Employers are hiring data scientists in every industry. In the competitive working landscape, which sees an endless flow of information and communication, data scientists help major decision makers shift from ad hoc analysis to an ongoing conversation with data. Thus, thousands of data professionals have made their mark at both startups and well-established companies. As they make their discoveries, they communicate what they learn and make suggestions for possible business directions and ongoing organizational processes. Their sudden appearance, mostly in enterprise environments, is a testament to the fact that companies are wrestling with various information sources in volumes never encountered before.
Whether you like financial markets or biopharmaceuticals, robotics or media, data scientists are present everywhere—even the United States government recently appointed a Chief Data Scientist. Networks connect data scientists with nonprofits doing amazing and meaningful work, and these positions are in demand at both established companies and startups. You don’t need to pick an industry or domain; you can keep your options open with data science.
Last but not least, a career in data science makes you a better decision maker in other spheres of life. At its core, training in data science turns you into a data-driven decision maker. More than anything, what data scientists do is make informed discoveries when surrounded by copious amounts of information from several sometimes conflicting sources. They then structure this data and make an educated analysis.
When it comes to examining information, automation and other technological processes—such as machine learning, algorithms and analytic systems—are must-have items that will enable you to do your job effectively. Instead of relying on your gut or “expert” judgement, you develop a habit of searching for data to refute or validate your every hypothesis. You may start formulating these hypotheses in a business context (”we should go with landing page A over landing page B”), but as your skills mature, you will see hypothesis tests in every aspect of your life—“I should date person A over person B; I should live in city A over city B; I should pick job A over job B.” Becoming a better decision maker is perhaps the best investment you can make in your future.
About the Author
Sham Mustafa is the cofounder and CEO of Correlation One, a talent platform that focuses exclusively on data scientists. Before launching Correlation One, Sham directed operations at two specialty finance firms. He has also provided business advisory services to more than 600 small companies. He holds an MBA and MPA from Columbia University as well as a BA and LLB from the University of Madras, India.