By Troy Sadkowsky, April 20, 2012

Takeaway: The data scientist role is fast becoming the most sought after career of the technology world. We asked top data scientist Jake Porway from The New York Times about how he got his job, and his tips for success in the field.


Source: Flickr/luckey sun The data scientist role is fast becoming the most sought after career in the technology world. Companies like Google, Facebook, Amazon and LinkedIn are using data scientists to help them maintain that innovative edge in the digital data era. And now data and technology enthusiasts are aspiring to become data scientists the same way some musicians aspire to become rock stars. Perhaps that's why some people are referring to data scientists as the new rock stars of the technology era.

Unfortunately, this role is still so new that there's still a level of obscurity about it, which means many wannabe data scientists are driving their tour buses down the wrong road. Do data scientists deserve their rock star reputations? We dive into the world of data science with an interview with Jake Porway, the data scientist from the R&D lab at The New York Times.
Data Scientists: Tech's Rock Stars?
So why are data scientists being referred to as the new rock stars of the technology world? This analogy actually goes deeper than data nerds' desire to sound ultracool. Just like a rock star, a data scientist's career includes diversity, artistic freedom and adaptability. And like the rock stars of the entertainment world, the best data scientists tend to gain quite a following of people from all walks of the data and technology industry.

What a data scientist does is very diverse; just as musicians use different instruments, tools and techniques to play musical styles that are as disparate as jazz and death metal, a data scientist also masters a particular tool and field. There's style involved, too. And there is no right or wrong way of doing the job either - it’s about the impact the work has on other people.

When the Beatles wrote their songs, there wasn’t just one person dictating how every note on every instrument was to be played. They came together and jammed; through creative discovery they found songs that worked. It's the same for data scientists. They have to feel the rhythm, get into the groove and harmonize a solution. This is only possible with the right amount of artistic freedom to try whatever approaches, tools and techniques might come to mind in the moment - and the agility to make changes when something seems out of key.

Once a data scientist masters the core fundamentals, he or she becomes adaptable, and gains the confidence to provide solutions in other fields. We talk more about these core fundamental later. The point to make here is that once you master data science you can take the role to whatever field you want, because data is everywhere.

A data scientist’s ultimate goal is to create massive amounts of value for the largest number of people possible. While a data scientist works behind the scenes, it's not unlike playing to a large audience: the better you do the job, the more people you reach - and the more rewards you see.
Data Scientists Do What?
So what do data scientists do exactly? Let's go through this with an example that we all might be able to relate to.

Let's say you realize one day that you don’t have the same amount of energy in the day that you used to. So you set yourself a goal: to have more energy during the day. Now, that’s a pretty broad and ambiguous goal. So the first step as a data scientist is to remove some of that ambiguity and quantify this goal's measurability. There are methods for this. We won't go into the details here, but let's just say that you theorize that you are not getting enough sleep and therefore give yourself the sub-goal of getting eight hours of sleep each night.

Even though this goal is a bit more measurable and less ambiguous, it has its own challenges. You can't really start a timer once you fall asleep, and even if you start a timer after you hop into bed, you may not fall asleep straight away. In addition, it's hard to account for the times you wake up in the middle of the night. Finally, there are different types of sleep, such as deep sleep and light sleep. The bottom line is that it's difficult to measure sleep accurately and therefore even more difficult to measure its impact on your energy levels.

So what can you do? Well, as a data scientist you'd seek out the latest in technology and discover that there are sleep monitoring devices. And if you used such a device to measure and digitally record your sleep, you'd be able to get more accurate data about your sleep, and collect that data over time to plot out a graph.

This alone can give you greater insight into what's going on. The visual representation will give you awareness, clarity and direction. You will be able to see if you are reaching your goal of eight hours of sleep a night and, more importantly, be able to take action if you are not.

This is the basic job of the data scientist: To bring new ways of measuring and displaying data so that more awareness, clarity and direction is provided to those looking at it.

But a good data scientist doesn’t stop there. Once the data is collected, it can be integrated with any other measured activity that you do throughout the day. Integrate it with your productivity based on data from your task management system. Integrate it with your moods based on tweets and status updates. Integrate it with your health based on visits to the gym or weight loss. With the amount of data already available and the ease at which it can be captured the possibilities are endless.
How to Be a Data Scientist
Interested in a career in data science? Because data science is so new, we asked a top data scientist for the insight into the field. Jake Porway is a data scientist at The New York Times and the founder of Data Without Borders, which matches nonprofits in need of data science with freelance and pro-bono data scientists. Porway has a computer science background and a Ph.D. in statistics from UCLA. Here's what he had so say about how to get into data science, how to perform well, and how to avoid key mistakes in the field.

1. Get the Right Skills
According to Porway, getting into the field boils down to three key things:
Practical computing skills
Statistical skills
A desire to learn...

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