Tag Archives: DJ Patil

DJ Patil – Tips to Build a Career in Data

DJ Patil, former US Chief Data Scientist and data science legend, has a nice video with helpful tips for people looking to get into data science.

  • Strive for Curiosity
  • Follow Ethics and Security
  • Be part of a Team
  • Solve a Local Problem

3 Top Data Scientists Change Jobs

Three of the Top Data Scientists have recently changed jobs.

Name Former Company New Company Announcement
Hilary Mason Bit.ly Data Scientist in Residence @ Accel Partners Techcrunch
DJ Patil Greylock Partners VP of Product @ RelateIQ Techcrunch
Monica Rogati LinkedIn VP of Data @ Jawbone Techcrunch

How To Build Data Science Teams?

Companies everywhere are struggling to assemble data science teams. Here are a couple of videos to help answer the following questions and more.

  • How do you assemble a team?
  • What skills do you need?
  • Where do you look for data scientists?

      DJ Patil, one of the stars of the data science world, answers a bunch of great questions in this talk. It is a couple years old, but still relevant.

      What are the Characteristics to look for in a Data Scientist?

      • Curiosity
      • Passion for playing with data
      • History of having to manipulate data to solve problems


      What are the Key Data Science Skills?

      • Finding Data Sources
      • Working with large data sets despite constraints
      • Cleaning data
      • Merging data sets
      • Visualization
      • Building tools for others to use


      Where to look for data science team members?

      • Internal
      • Interns
      • Other fields (physics, neurology, sciences)
      • Academic counterparts


      Principles for Data Science Talent

      • Would we be willing to work on a startup together?
      • Can you knock the socks off in 90 days?
      • Will you be doing amazing things?


      David Dietrich of EMC just recently added some insight to DJ’s points about building data science teams. His philosophy is: Building data science teams is not the goal. Developing data science capabilities is the goal. The structure is not nearly as important as the work being done. Different organizations can be successful doing data science different ways. In the video he lays out the pros and cons of all the following strategies.

      Strategies to Assemble Data Science Capabilities

      1. Transforming – reposition/add/modify existing teams such as a reporting team
      2. Creating – just start from scratch
      3. As a Service – consultants or websites, new ones are appearing every day
      4. Crowdsourcing – competitions like the Netflix prize or Kaggle


      Now, go start developing data science capabilities!