Hiring a Data Scientist, Building a Data Science Team and Getting Hired as Data Scientist


Two great articles came out about science of Data Science employment.  The article How to hire data scientists and get hired as one, is written by Derrick Harris from gigaom.com. Harris gives six tips:
1. Know the core competencies
2. Know a little more
3. Embrace online learning
4. Learn to tell a story
5. Prepare to be tested (aka “Your pedigree means nothing”)
6. Exercise creativity

The second piece is a blog post from Hortonworks and is titled How to Build a Hadoop Data Science Team.  This post describes how the skill set of a data scientist is a mix between those of a software engineer and a research scientist (see above graphic).


NYU’s Data Science initiative pushes North East to forefront

NYU has announced a university wide Data Science initiative and the creation of the Center for Data Science (CDS). Columbia University got a head start with The Institute for Data Science and Engineering (IDSE), but NYU is offering a MS degree with plans to offer a PhD, something Columbia’s IDSE is not doing at this time.

This news comes on the heels of Massachusetts’ efforts to make Boston a big data capital.   Massachusetts has long been a leading center for academic research in data mining and big data through MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL).

There are many institutions across the US that have added Data Science degrees to existing programs. However, NYU is one of the first schools to create a center with the purpose of offering a degree, as opposed to only conducting research, in this field.

Ayasdi: Topological Data Analysis

These are older stories, but never the less, I wanted to post them as a reminder to myself and a heads up to others. Ayasdi has developed a data mining platform that is based on topological data analysis.  The company was co-founded my Dr. Gunnar Carlsson of Standford University and is based on Carlsson’s pioneering research in the field of Applied and Computational Algebraic Topology.

Why is this interesting? Because this is a completely new approach to data analysis. A lot of the methodologies used in data mining/machine learning/data science is based on statistics and probability. The use of algebraic topology sort of comes out of left field (abstract algebra is extensively used in cryptology and computer security). Ayasdi’s approach shows they are doing some very novel and innovative research.

Enough said, below are two great articles that do a better job than I can explaining what exactly Ayasdi does.

This first article comes to us from Derrick Harris who writes for GigaOm. Check out his great article, “Has Ayasdi turned machine learning into a magic bullet?

The last article is from the New York Time’s blog Bits. The post, “Ayasdi: A Big Data Start-Up With a Long History,” gives some background about the company.