Sunday, October 23, 2016

What a data scientist is like and where they go?

A report by McKinsey Global Institute predicts by 2018 there will be about 1.5 million more jobs than skilled data scientists to fill those jobs.
Top 5 cities for big data jobs (June, 2014):
  1. San Francisco
  2. Mclean, Va.
  3. Boston
  4. St. Louis
  5. Toronto
Big data professionals can be particularly difficult to find since many roles require a complicated blend of business, analytic, statistical and computer skills — which is not something a candidate acquires overnight. In addition, “clients are looking for people with a certain level of experience, who have worked in a big data environment. There aren’t a lot of them in the market
“What is this person going to be doing? Do you need the technical skills? Or is the quantitative/statistical expertise more important? Is this person going to be doing data modeling or making business decisions?” Kelley says. “In an ideal world, companies want all of it. But it’s not an ideal world.”

First data scientist at Pandora

2001, Gordon Rios was hired by Pandora as the first official data scientist. Three key lessons to take away.

The fully integrated Scientist

He’s completely fascinated with how people determine what to listen to, why and how their tastes and habits change.
He is a full-time member of a Playlist Team, which comes before he is a member of the data science team. He is embeded with a team of engineers, product managers, designers and others. Some companies have all of their DS sitting together, some have them working completely separately from the rest of the company; others follow a consultant-like model where DS parachute into projects temporarily.
Our mission is to make sure that artists to get listeners, and listeners to have the best experience possible.Both depend on getting people to try new music.And that depends on experimenting , collecting data and designing algorithms that push people outside of their musical comfort zones at the right pace.
You need people on operations, engineering, product, and scientists all coming at the problems from different sides, yet with a common vision for the service.
A consultant model would never work. When I first got started with data mining earlier in my career, I often worked as a consultant, and it’s very difficult to make progress on large-scale problems that way. You have to be part of the team to understand all the moving parts.
The best-case scenario is staffing data scientists that have good engineering skills. When DS can ship, you save on headcount and can turn data into meaningful products.
Rios brought ful-stack programming abilities, big data experience, and machine learning expertise to the table. He also had other critical skills that your first scientist needs: the abitliy to work autonomously, self-motivate and be accountable.
In most cases, good management is about lining up people’s skills to the company’s needs, but with data science, so much depends on having people be both skilled and interested.

The art of DS Management

“Of course there are times when you have to be a trooper and tackle a project
that is less than interesting yet critical to the company, but if you have
incredibly rich talent, matching them carefully with the right projects has
got to be what scientific management is all about,” Rios says. “Being able
to consistently do that separates good managers from okay managers.”
Even though they sit separately the majority of the time, the team holds regular
meetings and often rallies for lunch to talk about what they’re working on and
to have more informal conversations about ideas. A lot of solutions emerge outof these discussions. Slightly more formally, they schedule time to present
their projects and findings to their colleagues so they can ask or answer
questions, and/or share practices that might be helpful with other
“Success for a data science program is when people are happy, feeling challenged and fulfilled, and delivering important results. That’s when they are at the highest performance state, delivering the most value,” says Rios. “There are a lot of reasons to bring on junior or inexperienced scientists —
they adapt and learn fast — but you better have really solid management and mentorship in place.”
“The hallmark of being a good manager or collaborator is that everyone
wants you involved in their project.” For the perspective employee, they have to bea culture fit firs and thena skill fit. They have to really love the product. and know about the data challenges we’re interested in solving.

Communicate for Optimum Efficiency

“To be an effective data scientist, you have to realize your job isn’t just about the research. You have to quantify and qualify what you do in a way that makes sense across the whole company.
“There are thousands of things we want to try, but we have to work
within the vision for what we want the service to be.”
Building and maintaining a sophisticated A/B testing platform is a must.
Make it standard to foster this transparency every time someone new comes aboard.
This is why we’re so focused on recruiting scientists who are scientifically curious but also entrepreneurial.