Editor’s Note, 2018: Even several years after publication, this blog post continues to generate a fair amount of traffic and discussion, with many asking what our complete criteria are for discerning who is a data scientist. We posted the complete (and updated for 2018) criteria that we use for our data science salary studies, which you can find here, if you’re interested.


In June 2013, with the data science hype picking up steam, I took to my blog to write Data Scientists… or Data Wannabes? – a post bemoaning  the plague of professionals who had begun changing their titles to Data Scientist without any of the necessary qualifications. At the time, the Data Scientist title was still loosely defined, which resulted in confusion in the market, obfuscation in resumes, and exaggeration of skills.pinocchio-595468_1920

Two years later, and the trend has gotten even worse. With the media inflating Data Scientists’ already-high salaries (I’ve yet to see a newly-graduated data scientist making $300,000+, as has been reported), data scientists have captured the imagination of job seekers thinking that they can write Hadoop on their resume and get a 50% raise.

I’m here to tell you that from all of my conversations with data scientists and “data scientists” I’ve discovered four telltale signs that a professional is not a true data scientist:


1. Lack of a highly quantitative advanced degree – It’s incredibly rare for someone without an advanced quantitative degree to have the technical skills necessary to be a data scientist. In our data science salary report we found that 88% of data scientists have at least a Master’s degree, and 48% have a Ph.D. The areas of study may vary, but the vast majority are very rigorous quantitative, technical, or scientific programs, including Math, Statistics, Computer Science, Engineering, Economics, and Operations Research. 2017 Update – Although it is becoming slightly more common for data scientists to have a quantitative Bachelor’s or Master’s layered with a top-tier bootcamp instead of a PhD, without a strong foundation in a technically rigorous program, it is very difficult to master all of the statistical and computer science concepts and skills necessary to be hired as a data scientist.

2. No concrete examples of experience with unstructured data or statistical analysisLists of tools such as Hadoop, Python, and AWS need to be accompanied by projects that show those skills being put to good use. If a professional cannot provide clear examples of their experience with unstructured data, or mentions data science projects, but keeps their involvement very vague, then they are probably not a data scientist. If their specific role in or impact on a Big Data project is unclear, that is cause for concern. 2017 Update – This is also true for statistical analysis. If a professional has experience organizing and structuring large data sets, but little-to-no experience with statistical concepts and analytics, then they are likely a Data Engineer, not a Data Scientist.

3. Purely academic or research background – Now, this is not to say that someone with a stellar academic or research background won’t make a great corporate data scientist, but a key component to being a data scientist in a corporate setting is business acumen. Understanding how findings affect business goals and delivering actionable insights to leaders is critical to a data scientist’s success. Many research academics have exceptional data skills, but without strong business savvy they are not data scientists… yet.

4. List of basic business skills – If I see a list of tools on a “data scientist” resume like Omniture, Google Analytics, SPSS, Excel, or any other Microsoft Office tool, you can be sure that I will take a harder look at whether or not this professional makes the grade. These skills are basic business qualifications that, by themselves, are insufficient for most data science jobs, and are not indicative of a true data scientist.


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47 Responses to “4 Ways to Spot a Fake Data Scientist”

  1. James

    I’m going to say up front that I’m relatively young and have only been a data scientist for 5 months (I’m an Engineering Scientist / Operations Research graduate).

    While this post makes some good points I think that the first point is over-emphasized both in industry and the post. I have worked with a few Masters and Ph.D. qualified data scientists and one thing I have come to realise is that while the academic credentials can certainly be treated as an indicator of ability, there is nothing to suggest that the best undergrads couldn’t run circles around Masters students and probably most of the Ph.D.’s.

    I’m not bagging on Ph.D.s at all, but I don’t believe there is much to gain for top undergrads doing a Masters (apart from a perceived ability increase).

    I’m happy to be proven wrong on this, what are other peoples observations?

    • Jamie

      Mmm… Apart from the fact that the PhD study time (four years in my case) was spent painstakingly applying theoretical approaches I learned as part of my undergrad and MSc to real-world examples sponsored by an industry partner (not model examples chosen because the available resources and data fit the technique being taught), followed by authoring those results in such a way as to prove their use my field, being knocked back from peer reviewers on the basis of scientific rigour, reworking my analyses before finally being published before being rigorously grilled on subject by a board of experts.

      While I’m sure it’s entirely plausible that some graduates are more adept than some docs (because some people are better than others), in my field – Computational Biology – there’s absolutely no way that an someone with an undergrad degree could ‘run rings’ around ‘most’ post-docs as they simply wouldn’t have had the time to gain enough knowledge of their subject matter as well as the required mathematical and computational skill to do the right analysis in the right way. Rather, at the university where I did my PhD, I was paid to help teach MSc and undergrads in practical sessions.

      I would also agree that business acumen is also extremely valuable, which could not be learned in academia alone. What I would also infer from my professional experience is that the longer you work, the more you understand the limits your knowledge. Excellent graduates are often brimming with confidence in their ability – their enthusiasm is a big part of their appeal as employees, but take care not to dismiss others on the basis that they aren’t as good as you at the list of things you understand because while that list may seem fairly complete to you, someone with more experience may know where the gaps in your knowledge lie.

    • S Parker

      “probably most of the Ph.D.’s.” That’s not very rigorous, is it? If you’re such a great data scientist, isn’t rigour and evidence important to you? I thought that’s what data science was. Perhaps you should do a PhD.

  2. JX

    I am a bit surprised that you mixed computer science with quantitative sciences

    • Paul

      Most people would say that machine learning is a subfield of computer science. And machine learning is the quantitative part of data science.

    • Horton

      Computer science *is* a quantitative science. It is a branch of mathematics, which is particularly apparent in advanced computer science degrees. It is a common misunderstanding that computer science is just “being really good at coding/systems”.

      • Mario Abarca

        Computer Science WAS a branch of mathematics in the beginning. Now we have to deal with software engineering, operating systems, computer security, artificial intelligence, robotics and so on… things that are either experimental or engineered, but certainly not deduced by a mathematical theory. In fact, Theoretical Computer Science is to Computer Science what Theoretical Physics is to Physics. This why it is called Computer “Science”, not Computer “Theory”.

  3. George Danner

    Its a shame that the media have contributed to this confusion by equating data science with the use of R as applied to customer data. That’s not data science!

    To me data science is all about business problem solving, using models and methods. Problem Solving skills are easy to test for in the interview process by asking a candidate to walk through a *problem* from beginning hypothesis to ending solution. If the candidate can tell a story with data as punctuation, that’s a data scientist. Everyone else is a poser.

  4. Igor Kleiner

    “nullius in verba” – the motto of scientists

    We should not become a victim authority fallace.

    Your position is a simply speculations without scientific proofs , it is not a more than simple opinion.
    That worhless

  5. Bijo Samuel

    I agree with your point 1 and 2 but not 3 and 4. You are basically implying that a person can only be a “Data Scientist” if they are specifically focused on “business” and industry. I disagree, firstly, the word business is vague, what exactly do you mean by it and what particular business skills are you referring to. We live in an age of specialization, its unrealistic and even unwanted that a person should be an expert in everything. A data scientist is a person who has the expertise to wrangle with data and extract insights from data, that is their scope. In industry\”business” you would typically work alongside experts in a particular field and if you don’t have that then you need to request it.

    This person (Data Scientist\Analyst) can come from any background, one could be an academic who spent the last 5 years doing various quantitative analysis in a biology department. Another could be a physicist or a biologist. Irrespective of their area of focus, I think the important thing is their general skill-set that can be transferred and applied to other areas. I think the focus needs to be on what this general skill-set should constitute, for me personally, at the bare minimum, the person should have a solid understanding of probability and statistics. But probability and statistics are not sufficient, in addition, the person should have a reasonable mathematical maturity, they don’t necessarily have to have mathematics degrees or phds, but a firm grasp of the critical undergraduate math which is Calculus (Single and multiple variable), Differential equations and Linear Algebra. In addition, it wouldn’t hurt to have knowledge of new machine learning developments, operations research, Combinatorics etc.

    In addition to the conceptual knowledge, they need to have experience applying it using one or more tools and a portfolio of actual projects (irrespective of field of application) showing how they have successfully derived “valuable” insights from data (extra points for deriving counter-intuitive insights). In addition they need to be able to wrangle with large amounts of unstructured dirty data. I would give each applicant a scenario where they are given a large amount of unstructured dirty data and question their approach to the project.

    Rather than business skills, I would emphasize that they have good communication skills (verbal and written) and most importantly, the ability to communicate with non-academics\non-technical people. In addition, the ability to sell is critical no matter what you do, you need to be able to sell yourself, your ideas, your insights and be responsibly for the “buy in”. You can’t just put something out there and hope someone will buy it.

    Most importantly, I think the person needs a hacker like attitude that is not limited in a dogmatic manner to only certain methodologies\systems or tools. I find most statisticians to be very much the opposite of this hacker spirit.

    • Carlos Perez

      I’m sorry, “strong business savvy”?

      What a bogus requirement. You can’t demand a requirement that is extremely vague. You may have domain experts in your team for a specific industry, but I wouldn’t call this ‘business savvy.

      I speak to folks with MBAs, but they have absolutely no clue about the software industry. Does you concept of “business savvy” even translate to all kind of different industries?

      • sbosowski

        Business savvy does not imply an MBA. We get into more detail about the softer skills necessary for success as a data scientist in this blog post, specifically:

        To be a data scientist you’ll need a solid understanding of the industry you’re working in, and know what business problems your company is trying to solve. In terms of data science, being able to discern which problems are important to solve for the business is critical, in addition to identifying new ways the business should be leveraging its data.

  6. Dennis Crow, Ph.D., PMP

    I would certainly agree with the comments posted. In the social sciences and public administration, “business” acumen is very important when it comes to the fundamentals of data. Logic and semantics are more important to getting across the importance of data. It is important to understand what you’re measuring and why. In the federal government, after 20 some years in domestic policy agencies (not scientific ones) I haven’t seen advanced statistics used where they are much needed. Knowing R or Hadoop will not provide that kind of knowledge or experience. With those kind of salaries, it’s tempting to leap into this, but with the proviso that one needs to know why any of that is applicable. Graph databases and SPARQL are probably more appropriate to administrative data. Etc. I’ll keep watching for comments and always more on this subject.

  7. Gene Leynes

    This is pretty funny coming from a recruiting firm that only did SAS recruiting in 2012 and said that they had no experience with people using R.

  8. Eric Zambrana

    I’m surprised there hasn’t been an effort to apply different levels of expertise to data scientists. Something like what they have for project management skills like green belt versus a black belt. The black belt would be the full fledged data scientist and so on. I would say that you will rarely see data scientists with a PhD in stats, strong data mining skills and sharp business acumen. Two out of the three tools should be acceptable and the two must haves should be business acumen and data mining skills.

  9. Jade Cook, PhD

    First of all, “data scientist” has nothing to do with science. It’s just an ordinary IT job that everyone with an advanced degree in quantitative discipline can do. The reason you’ve seen so many people applying for “”data scientist” positions are two-fold: (1) this job become more popular because of data expansion; (2) data “science” for application, has a very flat learning curve and can be learned in a couple of months simply because it’s not about creation, it’s *mostly* about applications of existing algorithms and requires very little innovation. What’s really hard still remains hard, but very few data scientists actually master them. The word “scientist” is simply being abused here. I’ve spoken to data scientist who has not even heard of Leo Breiman, or Jerome Friedman while they talk about trees and boosting all the time. All they talk about is their own little experiences with the methods invented by the real creators, which they treasure very much, but unfortunately, extremely easy to acquire.

    As long as a candidate satisfies (1) & (2) I would say she or he already deserves a second look. Just ask yourself a question, do you really think the “data scientists” you’ve hired are super capable or super smart? Not at all. Can they do their work well? Most certainly. So data scientist is just an IT job everyone can learn quickly and stop being ridiculous about it.

  10. kuz

    I am a budding data analyst after completing a second master’s in Management Science (OR). After reading the points raised above by the author of the article, I can infer that the author expresses her own perspective of who a data scientist is. This necessarily do not reflect an absolute truth or reality and in some ways her definitions are contestable. The author tend to underestimate the competence and capabilities of those who are of “purely academic or research background”. She emphasises the centrality of business knowledge to the role of a data scientist. I don’t agree with that perspective. The Data scientist role should not be confused with that of a business analyst or business development people. Though these roles have points where they overlap but they are different. That a data scientist must have business acumen before he/she can actually be considered a true data scientist implies a narrow view of who a true data scientist is. While studying for my master’s degree, I see companies and businesses bring projects comprising large data set to academics/faculties (management scientists and operations researchers) to help solve complex business issues. Most of these academics do not have vast amount of business acumen but they could analyse dataset and explain insights and information to managers of these firms. Often times, companies have acted on insights provided by these academics and these have resulted in improved performance. By the way, what is business acumen? Are we turning data scientists to business strategist? I can infer that unless one has several years of working in industry as a data scientist and dealing with projects anything sure of that do not make one a TRUE data scientist.

  11. A S

    This is an example of how perspectives can completely change the underlying meaning of things. Data science has nothing to do with ‘business’. Everything is not even business. Data science is being applied on many non commercial projects which are targeted at things like saving lives etc., and not targeted towards business. It is absolutely strange that someone writing about data science seems to be presenting a perspective that is an unacceptable diversion from the true idea of data science.

    To summarise: Current application of data science to commercial settings is still quite limited. Progress of data science has been primarily through solutions to very domain-specific and niche problems. It is important to disseminate correct information regarding a topic, instead of throwing out perspectives derived from experience in very limited settings.

    Thank you.

  12. HarveyZow

    If someone can work with the problem and can add value, why describe them as fake ? Data scientist is just a name. Why force people to comply to your definition? You seem concerned to exclude people. If you had posed this as your view of important knowledge or skills, that would be quite different.

  13. Bahman R

    The problem with companies who are looking for data scientist is that they want everything altogether cheap. They have been always like that. They are looking for bright minds with PhD-level quantitative skills and knowledge, yet they want them to have experience with unstructured data (as you put) or with market business. Well, I say good luck with that. Those features get together rarely. Scientists, mathematicians , physicists even many engineers are not familiar with unstructured data or market or business, they have spent most of their time on a huge amount of complicated knowledge and actually that’s why they are creative ! On the other hand people, specially computer engineers or IT professionals , many of them don’t know much mathematics. I’ve met computer science PhD’s who don’t know what a matrix is ! I suggest to you and many people who are in hiring, that value minds instead of techniques.

    • Paul

      It is every weird mate that a CS PhD doesn’t know what a matrix is??? I have 100 PhD friend all of them know and they are expert in various areas of Computer Science.

  14. Wilman

    Hello, whoever wrote the article is not well qualified. I say this as a person with years of both industry and academic experience. Particularly point 3 is rubbish. I can vouch that academicians are much better than the industry counterparts in terms of depth of knowledge in any area of data science and as such they will be much better data scientists. I have interacted with many industry working persons (HSBC, Barclays etc.) and found that they aren’t even good at feature selection.

    Just to prove what I said is true I urge all the readers to think about R and Python. R and Scientific python are groomed and many novel packages are developed by academicians. Today one could see that SAS (an industry standard) is miles behind R. Almost all major R packages involving different data mining tasks are from academicians in the form of publications. (particularly in Journal of Statistical Software). So, it looks funny when the person writing this article isn’t aware that data science has roots in academia and not in industry. Isn’t it damn wrong to say the academicians contributing to packages to tasks aren’t that suitable for data science? By the way dear author how many R packages have you contributed till date to public domain so that I can appreciate and accept your qualification to pass such a comment in point 3?

    Please accept my apologies dear author of the article if I sounded rude. Also, please mail me for any clarifications. I will try my best to answer any and all your questions.

  15. Fuen

    I really don’t think having a keen business insight is fundamental to being an awesome data scientist. And a cs phd not knowing what a matrix, is just borderline ridiculous and feels like a made up lie. Don’t think it is possible for someone to get an undergraduate degree without taking a math course on matrix/vectors.

  16. A Real Data-Scientist

    This article is a crap and meant to sooth people with undergrad and/or masters degrees titled as “Data-Scientists”.

    Term “Scientist” is awarded to some chosen people which means something. A scientist (whether a “data-scientist” or any other) cares about answering “Why” before “How”. A data-scientist is more into understanding the very intrinsic nature of data. His/her analytical minds constantly tries to find underlying patterns in data.

    Ph.D. means Doctor/Doctorate in Philosophy. It is awarded to those who have demonstrated/proven and have accepted abilities (by well-known scientific community) in a particular field. These abilities are demonstrated not just once, and not just in one way…these are proven over and over in different ways. Some examples to prove these abilities are:

    (1) First getting into a reputable Ph.D. program at a competitive university. Just getting there is a big challenge. You need a track record of good undergraduate and/or masters degree, references, etc.
    (2) Winning funding from competitive sources, such as, NSF, NASA, DoD, DoE, etc.
    (3) Ability to teach undergraduates by performing TA duties.
    (4) Being a research fellow/assistant for someone who has a lot more knowledge and experience than you.
    (5) Win funding to attend conferences and to present your research-findings before scientific community.
    (6) Win funding for scientific workshops, camps, etc.
    (7) Pass advanced level courses in your disciplines.
    (8) Pass qualifying/comprehensive exams in your areas of research.
    (9) Be known to current scientific community AND to know current scientific community.
    (10) Develop some meaningful scientific methods and discoveries.
    (11) Publish your findings in reputable conferences and/or journals (not some crappy, low level conferences/journals).
    (12) Convince scientific and/or federal organizations to give you research grant (which is very big achievement as you are evaluated by your peers anonymously and they trust your abilities by providing your funding coming from tax-payers money).
    (13) Graduating with a Ph.D.. It is estimated that only 64% Ph.D. students actually graduate with a Ph.D. degree in the USA. Most drop-out as they are unable to complete above steps. They complete only a few, but not all so they fail. Also, there are only 1% PhDs in the USA. So earning a Ph.D. from a reputable institution under the supervision of a real scientist is a big achievement. It changes your title from Ms./Ms. to Dr. Does that mean something to undergraduates/Masters?

    I can continue defining characteristics of a “Real Scientist”. Giving someone a title of “Data Scientist” without him/her having any solid track-record listed above is a joke and an abuse to scientific community. Above mentioned scales are standard in most universities. It means that not everyone has ability to sustain pressure of carrying out research and prove himself/herself. This scale filters out those who do not deserve to be a scientists. It could be due to personal problems or perhaps nature did not give them ability to cross that line which separates a “Real scientist” from a “Non-real scientist”.

    It is said that when you can not give someone monetary promotion, give them title (recognition) award so that they calm down and feel better about themselves. Most IT companies now-a-days award you “Data-Scientist” position even if you have an undergraduate/masters degree.

    Being able to perform some basic statistical analysis, writing regression/classification/clustering model in R, Python, etc. does not make you a data-scientist. These are just tools. Everyday new tools appear in market. Nothing special about them. You can for sure call yourself a “Data-Analyst”, but trust me, if you meet an actual scientist, s/he will be humored to hear you calling yourself a “Data-Scientist” with an undergraduate/masters degree.

    Finally, a real data-scientist does not give a damn about business as stated in this article. For a data-scientist a data is just a mixture of numbers, characters, sentences, etc. Data-scientist in interested only in finding hidden patterns in data. It is similar to what a patient is to a medical doctor; just a subject who has some known/unknown symptoms. Doctor’s job is to treat those symptoms whether that patient is the president or a criminal.

    • Christopher E. Devito

      absolutely true… a phd in computer science or any other natural science is very difficult to attain. much more complicated then landing a data scientist role.. it really is a bad job description ..

    • Suhaib

      The list that has 13 bullet points – well you don’t need ALL of these to have a PhD. (Those who drop out typically do because of several reasons, but that’s another discussion). I was enrolled in a PhD program, and I did not have to do #5,6,9 and 12. (Also you seem to be mixing “smart” with PhD – I can assure you a PhD is not about being smart, mostly a grinder. My adviser said this to me more than once – and yes he had a PhD from one of the top 5 schools possibly on the planet. However I had to drop out because of my toxic relationship.)

      The term data scientist is misleading for sure – but not all PhDs are “scientists” either – in the truest sense of the word. A PhD in Mechanical Engineering is not necessarily a scientist.

  17. Ted

    There are also a lot of fake data science blogs out there, selling low quality data science courses just wanting to make a quick buck off of people who think taking a course or two will qualify them for jobs which is very unlikely. Be careful out there, there’s people who know nothing about the field trying to squeeze money out of people.

  18. M. Norris

    Real: Your post reads like an infomercial. All the PhD tells me is that the holder are smart and/or rich or both. Obviously they are an outstanding academician, and can pass exams and qualifiers. That doesn’t automatically spell business value. Most PhD’s I’ve seen have been lousy teachers, and the thesis writing skills don’t always translate over to communicating with the real world. Far cry from the “proven” track record of which you speak.

  19. Sonam Jain

    Very Impressive Data Science tutorial. The content seems to be pretty exhaustive and excellent and will definitely help in learning Data Science course. I’m also a learner taken up Data Science training and I think your content has cleared some concepts of mine.

  20. Steve

    I’ve interviewed hundreds of data science candidates, and hired only a handful. I’d like to add the biggest way I see candidates fail.

    Bluntly put, many “data scientists” have no demonstrated business value. This is not meant to be harsh, but it is what it is. Many of the candidates I speak with have all kinds of fancy algorithms and research listed, even at big name companies (Google, Facebook, Amazon, etc). My question to them then becomes “so how did this project impact the business?”

    More than 90% of the time, the so-called data scientists did not actually put their models into production. In the rare occasion that they did implement their model at the production level, they don’t know what the impact was. If I’m going to be hiring someone for between $130,000 and $180,000 a year, they had better have not only real experience driving revenue, but also care enough about what they do to find out how their projects impacted the business. So many of these data scientists are just clueless about business and use buzzwords to get interviews, it’s really astounding.

    Bottom line: you cannot trust a resume from a data scientist. Call them out on their projects. Insist on knowing the impact. Insist on talking about concrete examples of models that they saw implemented from start to finish.

    The data scientist title is very sexy and alluring today, and a lot of companies are willing to pay a lot of money for it. This means that a lot of people are incentivized to exaggerate and even make up their experience. I’ve avoided making expensive hiring mistakes by doing my best to make sure that we only hire data scientists who can provide real business value.

    • Amirali

      “More than 90% of the time, the so-called data scientists did not actually put their models into production.”

      This is a fair point, but especially for ones with only internships or a year or two of work experience, this is often not as straightforward as “laziness” or “not delivering”. At a company the scale of Google for example, deploying a model to a multi-billion dollar system is several months of engineering investment, and potentially more in long term bet. It’s not uncommon to hear of people spending 3 months on a fully vetted and scoped project, getting in exact results and metrics and then the final decision is made to shelve it in favor of something else. This especially holds true for intern projects, where often an investment is ventured in greenfield projects.

      The scale is much different when you’re at a small startup with orders of magnitude. You’ll see a lot more “business value” models being delivered, but often its because there’s lower hanging fruit.

      For myself even, I’d say I’m prouder of some of my data science projects that I completed but never got deployed to production than smaller ones that did go out and deliver “business value”, simply because the scale of problem and research were non comparable.

      Now that said, if you start seeing someone with years of data science on their resume but no actual projects deployed to production, then that is of course a red flag.

  21. shivangi

    Great article sir! although I have recently started my career, I Know how data science being one of the most lucrative and highest paid jobs created a bubble of just knowing the names one can go and get paid what they just have started learning. Being a data scientist requires not only the skills of using the various programming tools but also one should have to be capable of analyzing and interpreting the results to the respective companies and also the real-time problems are the one which you get familiarized with experience not with only learning. A real-time driving experience makes the data scientists the real assets for the company. Respective author you have very precisely and correctly mentioned the real data analysts problems of identifying the real and the fake ones.
    In real meaning, data science is a blend of insightful and deep learning and knowledge about analytical tools like big data, machine learning with the goal with the goal to discover hidden pattern from the raw data and primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics, and machine learning.

  22. blahblah

    I’m just coming out of college with masters in IS.
    The dark truth is that many colleges are scrambling to play “catch up” with data science needs in industry. At first, IS degrees were all about systems development. Then analytics was a big thing in 2000+, so they scrambled to create specialized analyst degrees. Then data science became a big thing in 10’s +, and now colleges are trying to quickly shift gears again.
    Where I went to college, they had a data mining class, data sci class, and big data class.. but no data sci degree. And, teh stuff they taught was a hodge-podge of market basket analysis, confusion matrix analysis, scikit stuff, hadoop, etc. And, everything taught was prefaced with “this is just an introduction, if you want to be employable, you’ll need to deep dive on your own or LIE at interviews to say you have more experience then you have and LEARN ON THE JOB.”
    The Bach of IS itself was handing out the basics in databases & programming… I had been in the industry for years before heading back for my degrees, and I realized that the Bach was only good enough to get tech support work. But, folks would fluff up their resumes to get developer and DBA jobs.
    I went on to get my masters. They were in the process of getting rid of advanced systems development classes, and replacing it all with analytics.. and struggling to bolt on data science. I wanted systems development. Ended up having to take tons of analytics classes, because they were deprecating their sys dev classes. Took data sci classes and quickly learned I was hitting my level of incompetence.
    The thing was.. that most of these classes bolted on some “industry tool” to go with it.. Tableau, SciKit, etc. So, grads would just slap these tool names on their resumes hoping to get a job. But, having industry experieince, I knew there was a difference between learning / using a tool, and knowing the high-level stuff to really leveage it well. EG: Anyone can learn Python and code some Python sci-kit.. but that doesn’t mean you have the knowledge to be a Data Scientist.
    So, the industry has been flooded with “Data Scientists”… mostly people chasing a fat paycheck.
    To be fair.. the industry hasn’t been helping, either.
    I’m looking for jobs, and many companies are wanting someone that does analytics, dba and data science.. all rolled into one, but pay them $60k/yr. Companies are being absolute tight-wads in hiring, trying to squeeze blood from turnips.
    I had one interview where I had to very clearly explain to the department heads the difference between dba, analyst and scientist in a way that made sense to them for them to realize how insulting it was to ask for someone to do ALL of that at once and get paid peanuts.
    A lot of hiring folks are just like “oh, data science is a new buzzword.. slap that on the requirements”. Or, they’re looking to hire a post-grad for peanuts taht actually does know real Data Sci, and abuse them to train the rest of their staff for free.
    I myself know I’m not a data scientists even though I’ve been exposed to it. I hit my level of incompetence with it. I LOVE DBA & Analytics work, but..nobody wants to hire those now. They out-source routine DBA stuff to India, and if the hire an analyst they want them to be some super-mix of stuff.. eg: Accountaint with IS skills, or IS person with years of experience in Accounting analytics.
    Some jobs are asking specifically for data analystis.. but then go “must have degree in HR”. And I’m like “everything you described is the exact reason why you’d hire an IS person!”
    There’s just this huge disconnect between job seekers, schools, students and employers right now.

  23. Nishigandha

    Great article Linda! I agree that it’s critical to identify fake data scientists on the basis of academic background or skills mentioned on the resume. In fact nearly 85% of candidates lie on resume. I think the best way to identify fake data scientist is to give them online assessment like online data science test and data interpretation test to evaluate their practical skills. Recruiters or hiring managers can select only those candidates who have performed well in these tests and invite them for the interview process. This will help them to separate candidates with good practical skills from the one who are faking it.

  24. Wandem

    Just wondering if Linda’s perspective on who a true data scientist is more than four years later? Has her method of filtering data scientist made the clients she hires data scientist for given her any feed back as to the quality of data scientists she hires? How is the filtration process going on given that many tools have flooded the arena within the past four years? Is listing of excel in a data scientist’s resume a trap four years later? Probably listing R might be one catch since it has been there much longer and continuous to be? I feel that your approach narrows the scope of data scientist/analysts. Understand that structured and unstructured data calls for different tools. If you have learnt your excel well, you can do a lot of your work as a data analyst or data scientist. It will be helpful if you revisit this article today and help us understand your current position.

  25. littledidweknow

    Ironically, everything you wrote is what companies are trying to hire. Well, let me clarify.. real companies that want to hire real data scientists will foot the $100k to get them. Other companies are trying to hire “analysts” for $50k, but list all the tool skills (Tableau, SSRS, Power BI, Python) which make it clear they’re trying to get a cut-rate data scientists. Some are even bold enough to say “want someone skilled in machine learning, etc.”

    I’m out of college with a Masters in IS. I have a background in analytics, and would like to go back to doing that. But, every job I see is for data science. Even analytics jobs are just data science in disguise. I’m not qualified for any of them, because I lack the tool skills they ask for and I don’t feel I’m a real data scientist. (I was exposed to it in college, but it wasn’t my cup of tea. I prefer analytics and data governance / administration).

    But, the hiring market is nuts. Indeed is especially a trophy case of “ask for the moon” job postings. Type in the word “analyst” for jobs, and half are Business Analyst (project management) and the other half are “we want a data scientist, but want to pay you peanuts for it.”

    It’s just discouraging right now.

  26. blahblah

    Many companies are using “Data Science” & “Data Analyst” interchangeably these days. When I see a job desc asking for “Analyze data, must have SQL, Tableau, SAS, R, Python”.. that’s Data Analyst. But, they weed out the Data Analysts, b/c they now want R/Python, which are more Data Science tools. Companies expect Data Analysts to jump from Excel & BI tools to now becoming full-fledged programmers in SAS / R / Python. If you are a great analyst, but lack those skills, sorry, unemployable. It’s because they’re taking the skills that are pushing data science, and expect analysts to just move on to data science. Meanwhile, they want to keep paying analysts peanuts, but now expect them to do data science. They want an analyst to come in and create dashboards in Tableau, do analysis in SAS, do data science in R & Python, and.. oh, by the way, also organize all our data in SQL Servers or NoSQL big data databases. It’s a ridiculous amount of qualifications for a $50k job.. Yes.. these companies want to pay $50k for all of this.. b/c they have no clue what they’re asking for. They want a trifecta data manager, data analyst, data scientist. They want a “full stack” data person, that can manage the DB’s, analyze data short-term, then do machine learning for long-term stuff. They want 3 to 4 $50k jobs rolled into a single $50k job.. all because Data Science has buzzworded the industry lately. Meanwhile, unscupulous folks just go take a boot camp in SAS or R or Python or Tableau, then say they’re an “expert” on it. But, these folks don’t really have the high-level knowledge to understand why they’re using the tools or the problems they’re tasked to solve. But, they get hired, because “omg, look at all the skills this person has!” Companies are hiring laundry lists of skills, then complain when there’s a lack of higher knowledge going with it. I’ve got a Masters in a Quant field, lots of analytics background, but because I don’t have 5 years experience in Tableau & Python, I’m unemployable. It’s just ridiculous. The data analytics industry has become a hot mess ever since a) data science showed up and every company wants to assume it’s the same as analytics, b) a ton of different tools showed up for analytics (you have to be an expert in SAS, Tableau, PowerBI, SSRS, and many more BI tools for companies to hire you, b/c they want you to hit the ground running.. they don’t want to hire an analyst, they want to hire a tool expert), c) companies starting merging data science req’s into data analyst roles (R, Python, Machine Learning, AI, Neural nets)… yeah, sure,, I’ll get right on coding that neural net right after I finish making this dashboard for the execs. It’s a hot mess, and it’s only getting messier.

  27. anirudh

    Wonderful post Linda. It is easy to spot someone with fake experience by asking them scenario based questions during interviews. or asking them how they tackled real time data science problems. This in itself will filter out a lot of fake candidates. I usually give a real time problem to them and ask them to write the code in front of me, so that I can find out whether they have a structured thinking process, which is a necessary quality for a data scientist.

  28. Roger

    Very true. I once interviewed with a “Data Science” manager at a non-profit research firm. It was clear that the woman was not at all technical – by her own admission, in fact – and relied heavily on her quantitative analysts (a math PhD, an econometrician, among others) to bear the brunt of the modeling and dirty data work. She was more in a project coordinator type of role. I later looked at her LinkedIn profile and found that she had no other degrees than a “BA in Anthropology.” I’m all for evolving in one’s career interests and pursuits and not being defined by a college degree major. But it’s incredible how many people are calling themselves “data scientists” these days without possessing the technical clout to back it up.



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