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With a growing demand for efficiency and accuracy, comes a growing demand for stellar data scientists. In fact, as of March 2015, there are over 60,000 job postings for data scientists on LinkedIn, and over 250,000 people listing data science as their profession.
From weeding through and analyzing relevant data, to predicting future consumer behavior, data scientists are often expected to be the experts on everything. Since that is near impossible, employees are tasked with finding balance and scaling their talents to be as efficient as possible, and employers are tasked with finding those rare candidates.
So what makes a great data scientist?
The best data scientists go-beyond degrees (PhD or MS or BS) and trending technical skills (being born and raised on Hadoop); they embrace a true passion for problem solving. It’s much harder to teach someone qualitative skills, such as communications and curiosity, than it is to teach the latest algorithm or programming platform. Great data scientists are constantly evolving their technical and problem-solving skills to anticipate the next big data breach or to catch a hacker before they get to the consumer.
Managers need to make sure they hire the right people for the job, whether they are just out of college or experienced candidates. It’s important to look for those who can embrace data and algorithms, but more importantly to look for those who love to solve problems with data and algorithms.
One of my best data scientists received a PhD in theoretical math, concentrating heavily on theoretical research, but he had a natural curiosity about data and a passion for finding new tools and platforms. He might not be the first person who recruiters would think to choose for a data science position, yet he has the right combination of skills, and mostly importantly the right mindset to succeed.
On the other hand, candidates to shy away from are those who are either overly analytical or overly technical. Traditional analytical methodologies are not enough when it comes to catching up with the recent massive growth of both volume and variety of data.
Looking at data by nature requires an analytical mindset. It’s important to find people who can roll up their sleeves to process data regardless of the format or size and who can also simultaneously push past the data, identify problems and find innovative solutions beyond the scope of just analytics.
Here’s more about what to look for in great data scientists:
1. A passion for solving problems. A data scientist needs to go beyond identifying and analyzing a problem – he or she needs to solve it. An abundance of data does not necessarily mean an abundance of good data. If you simply run data through a block of code, you won’t have a successful solution. The successful data scientists I have worked with don’t just process the biggest data or implement the most advanced algorithm, they solve the problem. It’s the people who have an innate drive to find solutions for the right problem that will be the most successful as data scientists.
Oftentimes good data scientists will take advantage big data to help solve real-world problems. Big data has been used to help personalize our travel experiences, predict cold and flu trends, and better protect our personal information.
Laurie Skelly of Datascope Analytics puts it well saying, “Many who are drawn to data science find most alluring the opportunity to work on a constant stream of new and challenging puzzles.”
2. Hard skills and soft skills. I like to describe this as having an open mindset to embrace data and algorithms, and also being willing to be flexible.
Usually when people talk about data scientists, they are heavily focused on hard skills. At PayPal, we see resumes with great background in technical skills, but there is often a missing piece: seeing past the data. People who see just numbers, often won’t be as successful in data science. Data scientists also need a level of business acumen and an open mind to understand what the underlying problem is.
An open mind goes beyond specific programing skills – a candidate doesn’t necessarily need to be a Hadoop or Java expert. If someone has an open mind, it is easy for them to pick up on new programming trends in the ever-changing space. Compare that to someone who only knows Hadoop inside and out: When the market evolves, they’ll be left behind.
3. A team player. As I already said, it is near impossible to be an expert at everything. That’s why it’s necessary to rely on the combined strength of your team to solve problems more efficiently. The best data scientist is a team, not just one person.
Teamwork has long been touted as a necessary part of a successful company, but designating roles and responsibilities to ensure success is equally as important. The best data science teams have a balanced mix of someone strong in technology with someone strong in analytics, boosted with someone strong in business knowledge and someone with a broader view of the latest research and development in academia and industry. This will enable the team to quickly identify the right problem, find the right data and algorithms to solve the problem, scale the results, and bring the solution to production quickly.
At PayPal, our data science team consists of engineers boasting strong technical skills, with a portion focused solely on research and innovation. The rest of the team has vast technical skills but prefer to hone in on their analytical skills and business knowledge.
Good data scientists (or employees for that matter) can’t stay in their own domains, or they won’t grow. Relating their technology know-how to business and being willing to work with people who come from different points of view is a must for future data scientists.
The market will continue to evolve. However, one view that has and will remain steadfast is that the customer must always be top of mind. A data scientist must ultimately be in tune with what will help our customers. The customer focus will continue to define what types of problems we look to solve.
In the end, a customer might not want to know the exact science that goes into their user experience at their daily news site or payment platform, but the best data scientists are behind their seamless, secure experiences.
Hui Wang is senior director of Global Risk Sciences at PayPal.
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