Having recently graduated from my PhD program in statistics, I had for the last couple of months began searching for work in the field of statistics. Almost every company I considered had a job posting with a job title of "Data Scientist". In fact, it felt like long gone were the days of seeing job titles of Statistical Scientist or Statistician. Had being a data scientist really replaced what being a statistician was or were the titles synonymous I wondered?

Well, most of the qualifications for the jobs felt like things that would qualify under the title of statistician.  Most jobs wanted a PhD in statistics ($\checkmark$), most required understanding experimental design ($\checkmark$), linear regression and anova ($\checkmark$), generalized linear models ($\checkmark$), and other multivariate methods such as PCA ($\checkmark$), as well as knowledge in a  statistical computing environment such as R or SAS ($\checkmark$). Sounds like a data scientist is really just a code name for statistician. 

However, every interview I went to started with the question: "So are you familiar with machine learning algorithms?"  More often than not, I found myself having to try and answer questions about big data, high performance computing, and topics on  neural networks, CART, support vector machines, boosting trees, unsupervised models, etc. Sure I convinced myself that these were all statistical questions at heart, but at the end of every interview I couldn't help but leave feeling like I knew less and less about what a data scientist is. 

I am a statistician, but am I a data scientist? I work on scientific problems so I must be a scientist! And also I work with data, so I must be a data scientist! And according to Wikipedia, most academics would agree with me (https://en.wikipedia.org/wiki/Data_science, etc. )


  Although use of the term "data science" has exploded in business
  environments, many academics and journalists see no distinction
  between data science and statistics.


But if I am going on all these job interviews for a data scientist position, why does it feel like they are never asking me statistical questions?  

Well after my last interview I did want any good scientist would do and I sought out data to solve this problem (hey, I am a data scientist after all). However, after many countless Google searches later, I ended up right where I started feeling as if I was once again grappling with the definition of what a data scientist was. I didn't know what a data scientist was exactly since there was so many definitions of it, (http://blog.udacity.com/2014/11/data-science-job-skills.html, http://www-01.ibm.com/software/data/infosphere/data-scientist/) but it seemed like everyone was telling me I wanted to be one: 


https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/
http://mashable.com/2014/12/25/data-scientist/#jjgsyhcERZqL
etc....the list goes on.


Well at the end of the day, what I figured out was "what is a data scientist" is a very hard question to answer. Heck, there were two entire months in Amstat where they devoted time to trying to answer this question: 


http://magazine.amstat.org/blog/2015/10/01/asa-statement-on-the-role-of-statistics-in-data-science/
http://magazine.amstat.org/blog/2015/11/01/statnews2015/


Well for now, I have to be a sexy statistician to be a data scientist but hopefully the cross validated community might be able to shed some light and help me understand what it means to be a data scientist.  Aren't all statisticians data scientists?



(Edit/Update)

I thought this might spice up the conversation. I just received an email from the American Statistical Association about a job positing with Microsoft looking for a Data Scientist. Here is the link: Data Scientist Position. I think this is interesting because the role of the position hits on a lot of specific traits we have been talking about, but I think lots of them require a very rigorous background in statistics, as well as contradicting many of the answers posted below.  In case the link goes dead, here are the qualities Microsoft seeks in a data scientist:


  Core Job Requirements and Skills:
  
  Business Domain Experience using Analytics
  
  
  Must have experience across several relevant business domains in the utilization of critical thinking skills to conceptualize complex business problems and their solutions using advanced analytics in large scale real-world business data sets
  The candidate must be able to independently run analytic projects and help our internal clients understand the findings and translate them into action to benefit their business.
  
  
  Predictive Modeling
  
  
  Experience across industries in predictive modeling
  Business problem definition and conceptual modeling with the client to elicit important relationships and to define the system scope
  
  
  Statistics/Econometrics
  
  
  Exploratory data analytics for continuous and categorical data
  Specification and estimation of structural model equations for enterprise and consumer behavior, production cost, factor demand, discrete choice, and other technology relationships as needed
  Advanced statistical techniques to analyze continuous and categorical data
  Time series analysis and implementation of forecasting models
  Knowledge and experience in working with multiple variables problems
  Ability to assess model correctness and conduct diagnostic tests
  Capability to interpret statistics or economic models
  Knowledge and experience in building discrete event simulation, and dynamic simulation models
  
  
  Data Management
  
  
  Familiarity with use of T-SQL and analytics for data transformation and the application of exploratory data analysis techniques for very large real-world data sets
  Attention to data integrity including data redundancy, data accuracy, abnormal or extreme values, data interactions and missing values.
  
  
  Communication and Collaboration Skills
  
  
  Work independently and able to work with a virtual project team that will research innovative solutions to challenging business problems
  Collaborate with partners, apply critical thinking skills, and drive analytic projects end-to-end
  Superior communication skills, both verbal and written
  Visualization of analytic results in a form that is consumable by a diverse set of stakeholders
  
  
  Software Packages
  
  
  Advanced Statistical/Econometric software packages: Python, R, JMP, SAS, Eviews, SAS Enterprise Miner
  Data exploration, visualization, and management: T-SQL, Excel, PowerBI, and equivalent tools
  
  
  Qualifications:
  
  
  Minimum 5+   years of related experience required
  Post graduate degree in quantitative field is desirable.
  


People define Data Science differently, but I think that the common part is:

practical knowledge how to deal with data,
practical programming skills.

Contrary to its name, it's rarely "science". That is, in data science the emphasis is on practical results (like in engineering), not proofs, mathematical purity or rigor characteristic to academic science. Things need to work, and there is little difference if it is based on an academic paper, usage of an existing library, your own code or an impromptu hack.
Statistician is not necessary a programmer (may use pen & paper and a dedicated software). Also, some job calls in data science have nothing to do with statistics. E.g. it's data engineering like processing big data, even if the most advanced maths there may be calculating average (personally I wouldn't call this activity "data science", though). Moreover, "data science" is hyped, so tangentially related jobs use this title - to lure the applicants or raise ego of the current workers.
I like the taxonomy from Michael Hochster's answer on Quora:

Type A Data Scientist: The A is for Analysis. This type is primarily concerned with making sense of data or working with it in a fairly static way. The Type A Data Scientist is very similar to a statistician (and may be one) but knows all the practical details of working with data that aren’t taught in the statistics curriculum: data cleaning, methods for dealing with very large data sets, visualization, deep knowledge of a particular domain, writing well about data, and so on.
Type B Data Scientist: The B is for Building. Type B Data Scientists share some statistical background with Type A, but they are also very strong coders and may be trained software engineers. The Type B Data Scientist is mainly interested in using data “in production.” They build models which interact with users, often serving recommendations (products, people you may know, ads, movies, search results).

In that sense, Type A Data Scientist is a statistician who can program. But, even for quantitive part, there may be people with background more in computer science (e.g. machine learning) than regular statistics, or ones focusing e.g. on data visualization.
And The Data Science Venn Diagram (here: hacking ~ programming):

see also alternative Venn diagrams (this and that). Or even a tweet, while humorous, showing a balanced list of typical skills and activities of a data scientist:

See also this post: Data scientist - statistician, programmer, consultant and visualizer?.
