What is a data scientist? 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.
  

 A: 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?.
A: All great answers, however in my job hunting experience I have noted that the term "data scientist" has been confounded with "junior data analyst" in the minds of the recruiters that I was in contact with. Thus many nice folks with no statistics experience apart from that introductory one term course they did a couple of years ago now call themselves data scientists. As someone who with a computer science background and years of experience as a data analyst, I did a PhD in Statistics later in my career thinking it would help me stand out from the crowd, I find myself in an unexpectedly large crowd of "data scientists". I think that I might revert to "statistician"!
A: I'm a junior employee, but my job title is "data scientist." I think Bitwise's answer is an apt description of what I was hired to do, but I'd like to add one more point based on my day-to-day experience at work:
$$\text{Data Science} \neq \text{Statistics},$$
$$\text{Statistics} \in \text{Data Science}.$$
Science is a process of inquiry. When data is the means by which that inquiry is made, data science is happening. It doesn't mean that everyone who experiments or does research with data is necessarily a data scientist, in the same way that not everyone who experiments or does research with wiring is necessarily an electrical engineer. But it does mean that one can acquire enough training to become a professional "data inquirer," in the same way that one can acquire enough training to become a professional electrician. That training is more or less comprised of the points in Bitwise's answer, of which statistics is a component but not the entirety.
Piotr's answer is also a nice summary of all the things I need to do wish I knew how to do in a given week. My job so far has mostly been helping to undo the damage done by former employees who belonged to the "Danger Zone" component of the Venn diagram.
A: There are a few humorous definitions which were not yet given:

Data Scientist: Someone who does statistics on a Mac.

I like this one, as it plays nicely on the more-hype-than-substance angle.

Data Scientist: A Statistician who lives in San Francisco.

Similarly, this riffs on the West Coast flavour of all this.
Personally, I find the discussion (in general, and here) somewhat boring and repetitive.  When I was thinking about what I wanted to---maybe a quarter century or longer ago---I aimed for quantitative analyst. That is still what I do (and love!) and it mostly overlaps and covers what was given here in various answers.
(Note: There is an older source for quote two but I can't find it right now.)
A: There's a number of surveys of data science field. I like this one, because it attempts to analyze the profiles of people who actually hold data science jobs. Instead of using anecdotal evidence or author's biases, they use data science techniques to analyze data scientist DNA.
It's quite revealing to look at the skills listed by data scientists.
Notice the top 20 skills contain a lot of IT skills.

In today’s world, a data scientist is expected to be a jack of all
  trades; a self-learner who has a solid quantitative foundation, an
  aptitude for programming, infinite intellectual curiosity, and great
  communication skills.


UPDATE:

I am a statistician, but am I a data scientist? I work on scientific problems so I must be a scientist!

If you do PhD you're most likely a scientist already, especially, if you have published papers and active research. You don't need to be a scientist to be a data scientist, though. There are some roles at some firms, like Walmart (see below), where PhD is required, but usually data scientists have BS and MS degrees as you can see from examples below. 
As you can figure from the chart above, most likely, you'll be required to have good programming and data handling skills. Also, often data science is associated with some level, often "deep", of expertise in machine learning. You certainly may call yourself a data scientist if you have PhD in stats. However, PhD in computer science from top schools may be more competitive than stats graduates, because they may have quite strong applied statistics knowledge which is supplemented by strong programming skills - a sought after combination by employers. To counter them you have to acquire strong programming skills, so in a balance you'll be very competitive. What's interesting is that usually all stat PhDs will have some programming experience, but in data science often the requirement is much higher than that, employers want advanced skills, knowledge of algorithms and data structures, distributed computing etc.
To me the advantage of having a PhD in stats is in the problem captured in the rest of the phrase "a jack of all trades" that is usually dropped: "a master of none". It's good to have people that know a little a bit of everything, but I always look for folks who know something deeply too, whether it's stats or computer science is not so important. What matters is that the guy is capable of getting to the bottom, it's a handy quality when you need it.
The survey also lists the top employers of data scientists. Microsoft is on the top, apparently, which was surprising to me. If you want to get a better idea of what they're looking for, searching LinkeIn with "data science" in the Jobs section is helpful. Below is two excerpts from MS and Walmart's jobs in LinkedIn to make a point. 


*

*Microsoft Data Scientist


*

*5+ years of Software Development experience in building Data Processing Systems/Services

*Bachelors or higher qualifications in Computer Science, EE, or Math with specialization in Statistics, Data Mining or Machine Learning. 

*Excellent Programming Skills (C#, Java, Python, Etc.) in manipulating large scale data

*Working knowledge of Hadoop or other Big Data processing technology

*Knowledge of analytics products (e.g. R, SQL AS, SAS, Mahout, etc.) is a plus.



Notice, how knowing stat packages is just a plus, but excellent programming skills in Java is a requirement.


*

*Walmart, Data Scientist


*

*PhD in computer science or similar field or MS with at least 2-5 years of related experience

*Good functional coding skills in C++ or Java (Java is highly preferred)

*must be capable of spending up to 10% daily work day in writing production code in either C++/Java/Hadoop/Hive

*Expert level knowledge of one of the scripting languages such as Python or Perl.

*Experience working with large data sets and distributed computing tools a plus (Map/Reduce, Hadoop, Hive, Spark etc.)



Here, PhD is preferred, but only computer science major is named. Distributed computing with Hadoop or Spark is probably an unusual skill for a statistician, but some theoretical physicists and applied mathematicians use similar tools.
UPDATE 2:
"It’s Already Time to Kill the “Data Scientist” Title" says Thomas Davenport who co-wrote the article  in Harvard Business Review in 2012 titled "Data Scientist: The Sexiest Job of the 21st Century" that sort of started the data scientist craze:

What does it mean today to say your are—or want to be, or want to
  hire—a “data scientist?” Not much, unfortunately.

A: Somewhere I've read this (EDIT: Josh Will's explaining his tweet):

Data scientist is a person who is better at statistics than any
  programmer and better at programming than any statistician.

This quote can be shortly explained by this data science process. The first look onto this scheme looks like "well, where is the programming part?", but if you have tons of data you have to be able to process them.
A: I have also recently become interested in data science as a career, and when I think of what I learnt about the data science job in comparison to the numerous statistics courses that I took (and enjoyed!), I started to think of data scientists as computer scientists who turned their attention to data. In particular, I noted the following main differences. Note though that the differences appear mood. The following just reflects my subjective impressions, and I do not claim generality. Just my impressions! 


*

*In statistics, you care a lot about distributions, probabilities, and inferential procedures (how to do hypothesis tests, which are the underlying distributions, etc). From what I understand, data science is more often than not about prediction, and worries about inferential statements are to some extent absorbed by procedures from computer science, such as cross-validation.

*In statistical courses, I often just created my own data, or used some ready made data that is available in a rather clean format. That means it is in a nice rectangular format, some excel spreadsheet, or something like that that fits nicely into RAM. Data cleaning surely is involved, but I never had to deal with "extracting" data from the web, let alone from databases that had to be set up in order to hold an amount of data that does not fit into RAM anymore. My impression is that this computational aspect is much more dominant in data science.

*Maybe this reflects my ignorance about what statisticians do in typical statistical jobs, but before data science I never thought about building models into a larger product. There was an analysis to be done, a statistical problem to be solved, some parameter to be estimated, and that is it. In data science, it seems that often (though not always) predictive models are built into a larger something. For instance, you click somewhere, and within milliseconds, a predictive algorithm will have decided what is being shown as a result. So, while in statistics, I always wondered "what parameter can we estimate, and how do we do it elegantly", it seems that in data science the focus is more on "what can we predict that is potentially useful in a data product". 
Again, the above does not try to give a general definition. I am just pointing out the major differences that I have perceived myself. I am not in data science yet, but I hope to transition in the next year. In this sense take my two cents here with a grain of salt. 
A: I always like to cut to the essence of the matter.
statistics - science + some computer stuff + hype = data science

A: I say a Data Scientist is a role where one creates human-readable results for business, using the methods to make the result statistically solid (significant). 
If any part of this definition is not followed we talk about either a developer, a true scientist / statistician, or a data engineer.
A: I've written several answers and each time they got long and I eventually decided I was getting up on a soapbox. But I think that this conversation has not fully explored two important factors:


*

*The Science in Data Science. A scientific approach is one in which you try to destroy your own models, theories, features, technique choices, etc, and only when you cannot do so do you accept that your results might be useful. It's a mindset and many of the best Data Scientists I've met have hard-science backgrounds (chemistry, biology, engineering).

*Data Science is a broad field. A good Data Science outcome usually involves a small team of Data Scientists, each with their own speciality. For example, one team member is more rigorous and statistical, another is a better programmer with an engineering background, and another is a strong consultant with business savvy. All three are quick to learn the subject matter, and all three are curious and want to find the truth -- however painful -- and to do what's in the best interest of the (internal or external) customer, even if the customer doesn't understand. 
The fad over the last few years -- now fading, I think -- is to recruit Computer Scientists who have mastered cluster technologies (Hadoop ecosystem, etc) and say that's the ideal Data Scientist. I think that's what the OP has encountered, and I'd advise the OP to push their strengths in rigor, correctness, and scientific thinking.
A: I think Bitwise covers most of my answer but I am gonna add my 2c. 
No, I am sorry but a statistician is not a data scientist, at least based on how most companies define the role today. Note that the definition has changed over time, and one challenge of the practitioners is to make sure they remain relevant.  
I will share some common reasons on why we reject candidates for "Data Scientist" roles:


*

*Expectations about the scope of the job. Typically the DS needs to be able to work independently. That means there's nobody else to create the dataset for him in order to solve the problem he was assigned. So, he needs to be able to find the data sources, query them, model a solution  and then, often, also create a prototype that solves the problem. Many times that is simply the creation of a dashboard, an alarm, or a live report that constantly updates.

*Communication. It seems, that many statisticians have a hard time "simplifying" and "selling" their ideas to business people. Can you show just one graph and tell a story from the data in a way that everybody in the room can get it? Note, that this is after you secure that you can defend every bit of the analysis if challenged.

*Coding skills. We don't need production level coding skills, since we have developers for that, however, we need her to be able to write a prototype and deploy it as a web service in an AWS EC2 instance. So, coding skills doesn't mean ability to write R scripts. I can add fluency in Linux somewhere here probably. So, the bar is simply higher to what most statisticians tend to believe.

*SQL and databases. No, he can't pick up that on the job, since we actually need him to adapt the basic SQL he already knows and learn how to query the multiple different DB systems we use across the org including Redshift, HIVE and Presto - each of which uses its own flavour of SQL. Plus, learning SQL on the job means the candidate will create problems in every other analyst until they learn how to write efficient queries.

*Machine Learning. Typically they have used Logistic Regression or few other techniques to solve a problem based on a given dataset (Kaggle style). However, even that the interview starts from algorithms and methods, it soon focus on topics such as feature generation (remember you need to create the dataset, there's nobody else to create it for you), maintainability, scalability and performance as well as the related trade offs. For some context you can check out a relevant paper from Google published in NIPS 2015.

*Text Analysis. Not a must have, but some experience in Natural Language Processing is good to have. After all, a big portion of the data is in textual format. As discussed there's nobody else to make the transformations and clean up the text for you in order to make it consumable by a ML or other statistical approach. Also, note that today even CS grads already have done some project that ticks this box.


Of course for a junior role you can't have all the above. But, how many of these skills can you afford missing and pick up on the job?
Finally, to clarify, the most common reason for rejecting non-statisticians is exactly the lack of even basic knowledge of stats. And somewhere there is the difference between a data engineer and a data scientist. Nevertheless, data engineers tend to apply for these roles, since many times they believe that "statistics" is just the average, the variance and the normal distribution. So, we may add a few relevant but scary statistical buzzwords in job descriptions in order to clarify what we mean by "statistics" and prevent the confusion.
A: Allow me to ignore the hype and buzzwords. I think "Data Scientist" (or whatever you want to call it) is a real thing and that is distinct from a statistician. There are many types of positions that effectively are data scientists but are not given that name - one example is people working in genomics.
The way I see it, a data scientist is someone that has the skills and expertise to design and execute research on large amounts of complex data (e.g. highly dimensional in which the underlying mechanisms are unknown and complex).
This means:


*

*Programming: Being able to implement analysis and pipelines, often requiring some level of parallelization and interfacing with databases and high-performance computing resources.

*Computer Science (algorithms): Designing/choosing efficient algorithms such that chosen analysis is feasible and error rate is controlled. Sometimes this may also require knowledge of numerical analysis, optimization, etc.

*Computer science / statistics (usually emphasis on machine learning): Designing and implementing a framework in order to ask questions on the data or find "patterns" in it. This would include not only knowledge of different tests/tools/algorithms but also how to design proper holdout, cross-validation and so on.

*Modelling: Often we would like to be able to produce some model that gives a simpler representation of the data such that we can both make useful predictions and gain insight into the mechanisms underlying the data. Probabilistic models are very popular for this.

*Domain-specific expertise: One key aspect of successfully working with complex data is incorporating domain-specific insight. So I would say that it is critical that the data scientist either have expertise in the domain, be able to quickly learn new fields, or should be able to interface well with experts in the field that can yield useful insights about how to approach the data.

A: Data science is a multidisciplinary blend of data inference, algorithm development, and technology in order to solve analytically complex problems. But due to dearth of Data Scientists, a career in data science can really create numerous opportunities. However, organizations are looking for certified professionals from SAS, Data Science Council of America (DASCA), Hortonworks etc.  Hope this is a good information!
