Now that artificial intelligence and machine learning have become increasingly common tools in a business’ arsenal, it is equally important to have employees who are capable of using – or developing – such tools. Chief among them should be a data scientist: someone with the experience and ability necessary to work with structured and unstructured data, and build systems capable of mining that data to come up with actionable and useful insights.
The exact responsibilities of a data scientist can vary depending on the type of organization they work for, which means that it’s up to an individual business to determine what they are, and how they want to incorporate data science and machine learning into their company. In addition, data science is a relatively new field so, as a Forbes article put it, “large number[s] of data scientists are willing to apply yet few have the required experience.”
As careers in data science, artificial intelligence and machine learning grow increasingly lucrative, it is not unusual to see hordes of recent graduates looking for jobs in those fields. This, coupled with the dearth of data scientists with extensive job experience, means that for the time being, most companies will be hiring people who are either straight out of school or have limited work experience. When hiring someone straight out of school, it can be difficult to evaluate their qualifications, which means you will have to rely on other factors – how well they did in school, any research projects they did on the side, testimonials from professors or employers, etc. – to make a final determination. It would also be a good idea to offer some type of test to evaluate their coding and analytical skills (assuming, of course, that there is someone on your team capable of assessing such work).
On the other side of the coin, there are plenty of people with engineering experience who are looking to transition into data science, whether because of better pay or increased opportunity. These applicants potentially have more work experience, which might ease their transition into a new company – but beyond that, they also have (or should have) technical expertise that would aid in their data science or artificial intelligence initiatives. Whomever you choose, they must be able to understand and have experience with the latest machine learning and data handling tools, and with the tools and techniques that your particular organization uses. Not only will this help ease their transition into the organization, it will also allow them to fully exploit your data, and produce optimal results.
Curiosity is another key trait for a good data scientist. After all, they are not simply drones doing data entry; they’re researchers looking for unique ways to solve a problem or test a hypothesis. If they think they see a pattern, they have to be able to ask questions: Why does the data look like this? Is it because of how I set up the parameters of the algorithm, or does it represent actual insight? How does this information help either my organization or my clients achieve their goals? How might these results be improved?
Finally, a good data scientist needs to think logically and have good communication skills. Not only do they need to be able to test hypotheses logically until they arrive at a theory, they also need to be able to communicate that theory in a compelling way. If someone is unable to communicate their findings, they will not be able to explain to others why those findings are important, and influence people’s decision-making.
Given how quickly the field of AI has changed over the past few years, and the ever-increasing number of approaches that one can take to developing AI and machine learning, it is very easy for someone to find themselves out of the loop. Consequently, a good data scientist has the drive and inclination to keep up with the latest developments in the field, while perhaps also finding ways to integrate those innovations into the existing organizational infrastructure. And, from a business’ perspective, a truly great data scientist knows how to walk the line between innovation and business-savvy. This may sound like an impossible checklist, but it is worth taking the time to find the right data scientist whose values are aligned with the values of your business.
About the Author
Jeremy Fain is the CEO and co-founder of Cognitiv, the first marketing AI company to offer plug-and-play deep learning products that enable marketers to improve results through custom algorithms. Cognitiv’s award-winning technology creates and executes self-learning, fully automated deep neural networks for multi-touch, full-funnel marketing campaigns.
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