Data scientist -- it’s been called the sexiest job of the 21st century. As companies in nearly every industry strategize how to use big data for competitive advantage, professionals who can sift through and make practical sense of this information are becoming some of the most sought-after hires. Unlike some technical roles, though, this job is a multidisciplinary one. If crunching data sets and championing business decisions sound sexy to you, consider becoming a data scientist or at least adding some of the skills to your set.
Simply put, a data scientist’s job is to solve business problems using information derived from data the business collects. It’s a relatively new type of position made possible by the age of digital disruption. Companies amassing vast amounts of data from multiple sources -- everything from customer purchasing activity and sales figures to financial market data and weather statistics – know that this information can be a goldmine if properly processed. That’s the data scientist’s job, to organize and analyze these data to yield useful information for decision-making.
Data scientists must be broadly talented: success requires strong skills in math, computer programming, problem solving and communication. Their work runs from managing huge amounts of unstructured data to making predictions and recommending actions based on what patterns they find. It’s where data mining meets business strategy, and a company’s bottom line increasingly relies on a data scientist’s valuable work.
It’s the simple law of supply-and-demand. Data scientists are a small, highly-educated bunch – almost 80 percent hold masters or doctoral degrees – and there aren’t nearly enough of them yet to fill the voracious demand from tech companies and other data-driven organizations. Although the number of data scientists has doubled in the last four years, demand still way outpaces supply, according to a 2015 MIT Sloan Management Review and SAS study.
The result? Chunky salaries. A recent study by executive recruiting firm Burtch Works reveals that junior-level data scientists can expect starting pay of around $70,000, and top managers in the field up to $250,000. And there’s almost always a path to advancement if you’re good at it.
Because the position is a relative newcomer to the job marketplace, the number of degree programs in data science still lags behind. Colleges and universities are quickly catching onto the trend, however, especially with master’s programs. Today’s data scientists have varied educational backgrounds. Many are former physicists or software engineers who fill the gaps in their skill sets with extra training.
Regardless of previous experience, a candidate for a data scientist job needs, at a minimum, expertise in these areas:
Also important are strong presentation skills, particularly when communicating to non-technical types. You may extract highly meaningful information from customer call data, for example, but if you cannot articulate it in business-speak to the VP of Product, it won’t help the company going forward. Decision-makers should be made to understand what data were used and why, what patterns emerged in the analysis, and what conclusions can be drawn as a result.
Non-technical organizational managers and leaders in data-driven firms are keenly aware of the need to understand the fundamentals of data science themselves. While they don’t have to learn Python coding, they must be able to understand the methodology behind data management in order to ask the right questions, challenge the predictions, and recognize which kinds of problems can be addressed through big data science.
In many fields, proof that you’re competent at making decisions based on data analysis is a must to further your career. Resumes that demonstrate this stand out. Luckily, training programs in this space abound. Fordham University, for instance, hosts a 2-day course entitled “Big Data Analytics for Executives” to teach core concepts of what data scientists do all day. Coursera offers a 1-week online crash course in data science to bring leaders up to speed quickly.
If any of this piques your interest, you may want to explore data science as a profession. If it doesn’t, you may still want to consider taking a high-level overview course on big data analytics. The more you understand how data science can be used to predict outcomes in business, the more you may see how it can help your company. This kind of know-how will soon become all but essential in a professional’s skill set as big data increasingly shapes the way we make business decisions.
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