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How to Identify – and Build – Top Data Science Talent


June 22, 2018 - 0 Comments

Harvard Business Review has called Data Scientist one of the most in-demand jobs of the 21stCentury. Many businesses know how important this role is – so there is significant competition for top-tier talent. But it’s worth the effort, because these are the people who can overcome some of the limitations of traditional data analysis and deliver a huge amount of value in a very short period of time.

On my team, for example, we have a data scientist who solved a problem that will easily have a multimillion dollar impact over the course of the coming year, and he did it within his first five months on the job.

As I travel to various Cisco locations and industry events around the world, I’m often asked how to identify these top-level scientists, and what our partners should look for when hiring them. Here are four things to consider:

  1. Look for problem solvers: In building our team, I’ve learned that the most important predictor of success is whether someone wants to understand the business problem and figure out how to solve it using data. I look for passionate problem-solvers who want to make an impact. There are four key areas in which data scientists should excel: collecting data from various sources, understanding data and how to analyze it, understanding the problem and how data relates to it, and knowing how to implement predictive algorithms, i.e. predicting what is going to happen in business so that the organization can take appropriate steps. These tasks may sound simple on the surface, but with the massive amount of data that’s arisen in the last decade, they’re quite complex. And when you have a lot of data that’s been locked up and under-analyzed, and you bring in people who can start to get to that data, pull it together, and analyze it – you’re going to see big things happen.
  2. Strong software development skills: Today’s data scientists need to possess more technical skills than ever before. As you begin your search for a new hire, ideally, you should seek out someone who’s a strong software developer. He or she should have advanced knowledge of statistics and the ability to write and optimize Hive and Spark queries. They should also possess extensive knowledge of algorithms, machine learning and data mining, and the ability to provide a clear, creative visualization and presentation of data.
  3. Where to find talent: The reality is, it’s rare for a single person to possess all of the skills mentioned above, but good candidates will be technical – and passionate enough to want to learn them over time. And you don’t have to hire someone with a ton of experience. Nearly every university has an undergraduate and/or graduate computer science program, and those graduates can usually do some data science work and predictive analytics right out of school. Also, if you look within your organization to build talent, you may be able to find traditional data analysts with genuine interest in being trained to develop advanced data science skill sets.
  4. Soft skills for the win: Whether they come from the university or your own office, if the candidates are eager to develop and evolve their skills, it won’t take long before they’re contributing members of your team. As you consider your top choices, look for someone with indispensable soft skills like storytelling – so that they can explain what they’re doing to create interest, understanding, and greater influence for your efforts across the organization.

When you combine the right people with the right skills, don’t be surprised when they begin to unlock some staggering business potential. Lastly, don’t forget that Cisco is here to help. With Lifecycle Advantage and other partner-focused tools, programs and services, you can take advantage of our investment in data science to better serve your clients – whether it’s predicting and prescripting their business needs, accelerating their time to value using Cisco technology, or strengthening their success and your relationship with them.

 

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