[MUSIC] When the companies are hiring people for a data science team, maybe a data scientist or an analyst, or a chief data scientist, the tendency would be to find the person who has all the skills, that they know the domain-specific knowledge. They're excellent in analyzing structured and unstructured data. And they're great at presenting and they've got great storytelling skills. So if you put all this together, you will realize you're looking for a unicorn. And your odds of finding a unicorn are pretty rare. I think what you need to do to is to see, given the pool of applicants you have, who has the most resonance with your firm's DNA. Because you can teach analytics skills, anyone can learn analytics skills if they dedicate time and effort to it. But what really matters is who's passionate about the kind of business that you do. Someone could be a great data scientist in the retail environment, but they may not be that excited about working in IT related firms or working with gigabytes of weblogs. But if someone is excited about those weblogs, if someone is excited about health-related data then they would be able to contribute to your productivity much more so. And I would say if I'm looking for someone, if I have to put together a data science team, I would first look for curiosity. Is that person curious about things not just for data science but anything like, are they curious about why this room is painted a certain way, why do the bookshelves have books, and what kinds of books? They have to have a certain degree of curiosity about everything that is in their vision, that they look at. The second thing is do they have a sense of humor because, you see, you have to have a lighthearted about it. If someone is too serious about it, they probably would take it too seriously, and would not be able to look at the lighter elements. The third thing I think, and I think the last thing that I would look for if I had to have a hierarchy, the last thing I would look for are technical skills. I would go through the social skills, curiosity, and sense of humor. The ability to tell a story. The ability to know that there is a story there. And then once all is there then I would say, well, can you do the technical side of it? And if there is some hope or some sign of some technical skills, I would take them because I can train them in whatever skills they need. But I cannot teach curiosity. I cannot teach storytelling. I cannot certainly, instill sense of humor in anyone. >> I think there's no hard and fast rule for hiring data scientists. I think it's going to be a case by case thing. I would say there has to be some sort of technical component, somebody should be able to work with and manipulate the data. They should be able to communicate what they find in the data. I find quite often nobody really cares about the r-square or the confidence interval. So you have to be able to introduce those things and explain something in a compelling way. And they also have to find somebody who is relatable, because data science, it been typically new means that the person in that role has to make relationships and they have to work across different departments. >> If these data scientist has a good mathematics and statistics background.>> They have to consider like problem solving abilities and analysis. The scientist needs to be good in analyzing problems.>> The persons they are hiring, they should love to play with data. And then they know how to play with the data visualization. They have analytical thinking.>> When a company is hiring anyone to work on a data science team, they need to think about what role that person is going to take. Before a company begins, they need to understand what they want out of their data science team. And then they need to hire to begin it. As they grow a data science team, they need to understand whether they need engineers, architects, designers to work on visualization. Or whether they just need more people who can multiply large matrices. >> From a skills point of view, let's focus on the technical skills and in that case, first thing would be what kind of a technical platform would you like to adopt? Let's say you want to work in a structured data environment and let's say you want to work in market research. Then the type of skills you need are slightly different than someone who would like to work in big data environments. If you want to work in the traditional market research data, structure data environment, your skills should be some statistical knowledge and some knowledge of basic statistical algorithms, maybe some machine learning algorithms. And these are the tools that you would like to develop. If you want to work in big data, then there's the other aspect of it and that is to be able to store data. So you start with the expertise in storing large amounts of data. And then you look into platforms that allow you to do that. The next step would be to be able to manipulate large amounts of data, and the final step would be to apply algorithms to those large sets of data. So it's a three-step process. But most likely it starts, most importantly, it starts with where you would like to be, in what field, in what domain. In terms of platforms, let's you want to be in the traditional predictive analytics environment, and you're not working with big data, then R or Stata, or Python would be your tools. If you're working mostly with unstructured data, then Python is most suitable than R. If you're working with big data, then Hadoop and Spark are the environments that you will be working with. So it all depends upon where you would like to be and what kind of work excites you and then you pick your tools. In addition to technical skills, the second aspect of the data science is to have the ability to communicate. The communication skills or presentation skills. I call them story telling skills, that is that you have your analysis done, now can you tell a great story from it? If you have a very large table, can you synthesize this and make it more appealing that when it goes on the screen, or is it part of a document that it just speaks? It sings the findings and the reader just gets it right there. So the ability to present your findings, either verbally, or in a presentation, or in a document. So those communication and presentation skills are equally important as the technical skills are. When you have a grading side, when you're presenting your results, imagine you're driving on a mountain and then there's a sharp turn. And you can't see what's beyond the turn. And then you make that turn and then suddenly, you see a tremendous valley in front of you. And this great sense of awe, that I didn't know that, right? So when you present your findings and you have this great finding and you communicate it well, this is what people feel because they were not expecting it. They were not aware of it, and then this great sense of happiness that now I know. And I didn't know this, now I know. And then it empowers them, it gives them ideas, what they can do with this knowledge, this new insight. It's a great sense of joy. And you are able as a data scientist, you are able to share with your clients because you enabled it. [MUSIC]
