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  • - 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 are excellent in analyzing structured

  • and unstructured data,

  • and they are great at presenting,

  • and they've got great storytelling skills.

  • So if you'll 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 is to see,

  • given the pool of applicants you have,

  • who has the most resonance with your firm's DNA.

  • Because, see, you can teach analytic skills.

  • Anyone can learn analytic skills

  • if they would dedicate time and effort to it.

  • But what really matters is who is 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 exited about

  • working in IT-related firms,

  • or working with gigabytes of web logs.

  • But if someone is excited about those web logs,

  • or 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.

  • Are they curious about why this room

  • is painted a certain way?

  • Why the bookshelves have books and what kind 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 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 that I would look for,

  • if I have to have a hierarchy,

  • the last thing I would look for are technical skills.

  • I would go through these social skills,

  • curiosity, 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 will 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 gonna be a case by case thing.

  • I would say there has to be some sort of

  • technical component.

  • Somebody should be able to 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 squared or 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 being typically new means that

  • the person in that role has to make relationships

  • and they have to work across different departments.

  • - If this data scientist has a good mathematics

  • and statistic background.

  • - They have to consider problem solving abilities

  • and analysis.

  • A data 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, let's focus on the technical skills,

  • and in that case, first thing would be

  • what kind of technical platform would you like to adopt.

  • Let's say you wanna work in a structured data environment,

  • and let's say you wanna 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 wanna work in the traditional market research

  • structured data environment,

  • your skills should be some statistical knowledge,

  • some knowledge of basic statistical algorithms,

  • maybe some machine learning algorithms,

  • and these are the tools that you would like to develop.

  • If you wanna 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 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.

  • So, in terms of platforms,

  • let's say you wanna be in a 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 more 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 additional to technical skills,

  • the second aspect of the data science

  • is to have the ability communicate,

  • the communication skills or presentation skills.

  • I call them storytelling 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 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 that communication and presentation skills

  • are equally important as the technical skills are.

  • When you have a great insight,

  • and 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 notice. 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.

  • As a data scientist, you're able to share it

  • with your clients because you enabled it.

- When the companies are hiring

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B1 中級

數據科學的招聘[數據科學101]。 (Recruiting for data science [Data Science 101])

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    陳賢原 發佈於 2021 年 01 月 14 日
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