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Should you go to a bootcamp?

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The academics I’ve spoken with about leaving academia have frequently asked how they can quickly gain the skills and qualifications necessary to get a job in technology. They’ve heard about so-called “bootcamps” that exist specifically to train people in those skills quickly. But are they worth it?

In order to get into such a camp, applicants need to demonstrate already acquired skills and knowledge. Best of all, it is possible to describe it constructively in an interview essay, to increase the chances of a positive result, you can use the help of professionals.

What you get in a bootcamp

I never attended these bootcamps, but I’ve worked with about a dozen people who did, and I’ve interviewed at least a couple of dozen job candidates whose major qualification was that they graduated from a bootcamp of some sort.

With a few exceptions, these programs are designed for people who have little to no experience in the field. They aim to take their participants from zero to professional in a very short time — usually six to twelve weeks. They are very heavily focused on practical skills, and the coursework revolves around projects that are intended to be as close as possible to real-world problems that you’d typically see in an entry-level position. Those projects are usually suitable for putting on one’s resume. Class sizes tend to be small, and there is usually a lot of personal attention. There’s also a strong focus on working in groups on these projects, which is realistic because collaboration is so important in almost any job in technology.

Participants often get very useful job hunting assistance. Some have events at the end that are like miniature job fairs, with employers coming to the bootcamp’s offices to interview graduates on-site. In fact, some bootcamp graduates are prohibited from seeking a job elsewhere until the job fair happens. Additionally, participants typically will get advice about writing their resumes and other nuts and bolts aspects of job hunting.

These are very substantial benefits to attending a bootcamp. But they come with important trade-offs. In order to get a set of projects completed in such a short time, there is a very strong focus on using the languages and tools that automate as much of the work as possible. For example, the only way you’re going to go from zero to a working web application in a few weeks is by avoiding having to learn all the nitty-gritty details of how a web application actually works “under the hood”. You’ll have a professional-looking, fully-functional web application at the end of the bootcamp (and you’ll know how to develop them on your own), but you probably won’t know any of the implementation details that have been hidden from you by the development tools you’ve learned.

To take another example, there are now some reputable “data science bootcamps”, which are flooding the market with graduates. They have the same strengths and weaknesses as the others, but the weaknesses are more severe. Data science uses some high-powered algorithms and tools that often depend on graduate-level mathematics or computer science. There is no way on earth that anyone is going to learn the foundations of those tools in such a short time. However, there are a number of high-level libraries that have done the heavy lifting for you. If you want to use some technique like “principle component analysis”, you don’t have to understand how or why it works — you can simply import a pre-existing library into your code and run it on your data. In another minute or so, you can create a beautiful visualization of the results and run some statistical tests (which you also do not need to understand).

In these data science bootcamps, you’ll get a very broad, but shallow overview of the most important techniques that are commonly used in industry. You will also get a lot of practice using those techniques on real-world data. But if all your knowledge of data science comes from the bootcamp, you will certainly not understand how they work, why they work, or the assumptions underlying those techniques. There is simply no way to gain a strong enough foundation in such a short period of time.

In the case of data science, in particular, this is very dangerous. Data scientists make recommendations to businesses that can have dramatic effects. It is very easy to lull yourself into a false sense of security because you’re using the most powerful, modern techniques for data analysis. But those high-powered techniques come with assumptions which cannot be validated automatically by any tool. In order to have any reasonable confidence in your analyses, you have to understand both the business and the foundations of the techniques you’re using. If you don’t, you can easily drive a business right off a cliff.

Who benefits?

In order to benefit from a bootcamp, you’ve got to know yourself and your limitations. As I mentioned above, I’ve worked with and interviewed many bootcamp graduates. You can split them into two categories, and it’s very easy to do so.

In the first category, there are people who are absurdly confident because they have no clue that their education has only scratched the surface of their chosen field. Their resumes are overblown lists of impressive-sounding technologies. Indeed, their experience appears to be that of someone who’s been in the field for decades. But their knowledge is shallow. If you ask for anything beyond the scope of the bootcamp, they’re shocked to discover that they don’t know the answer. They know how to do things exactly one way, in exactly one environment. If you were to take away the tools that they were handed in their bootcamp, they’d be set adrift. They don’t even know how to go about finding out how to learn.

In the realm of data science bootcamps, I’ve discovered one question that seems to completely baffle people in this first category. I name a technique that is listed prominently on their resume, and I ask them to describe a case when that technique would not work. Nothing in data science works on all problems, so this is a fair question. It’s shocking to me how few job applicants for data science can answer this question at all. But it’s crucial. If you don’t know when a given technique won’t work, then you can’t responsibly use it, and nobody should have any confidence in your analysis. Being able to use a tool doesn’t entail that you know when to use it. In my highly unscientific sample, more than 90% of the bootcamp graduates I talk to can’t even begin to answer such a basic question. And keep in mind that these are people who have listed those techniques on their own resumes!

The second type of bootcamp graduate is made up of people who are conscious of their own strengths and weaknesses. If you ask them what they liked and didn’t like about their bootcamp experience, they’ll say that they got a good start on learning a lot of practical skills, but that they’ve got a lot more to learn. They see their training as giving them a path forward as they continue their professional development. They’re curious about what they don’t yet know, and they are enthusiastic about learning. Rather than being satisfied with the skills they’ve learned, they’re more curious and intellectually engaged than they were before. These people have benefited a lot from their bootcamp training, and they can be excellent colleagues.

Beware!

Bear in mind that bootcamps are for-profit businesses, and they are not cheap. You’ll usually spend at least several thousand dollars to attend one. Don’t believe that you’ll emerge with the knowledge of someone who has years of professional experience. When you decide whether to hand over your money, be realistic, and be humble. Talk to others who have gone through the training. And if you do decide to try one, remind yourself that it’s just the first step of your career, not the last.

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Interviewing when you don’t check all the boxes

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If you’re transitioning out of academia into the private sector, then the following scenario is likely to occur: You apply for a job that carries with it a laundry list of very specific skills (some of which are “required”), of which you have few or none. Despite this fact, you get an interview. You’re terrified of the interview, because you dread the prospect of saying “I don’t know” in response to most of the interviewer’s questions.

This happened to me several times. I’d see an advertisement for a job that required experience in technologies I’d never used; I’d apply for it anyway and get an interview. There’s no good roadmap for interviewing under those conditions. But if you’re coming from an academic background, this is the experience you’re overwhelmingly likely to have.

I learned pretty quickly that these interviews fall consistently into one of two categories. If the interview is going to be bad, you’ll get someone who proceeds to ask you a bunch of specific questions that you don’t know the answer to. This is frustrating because (assuming you’ve been honest — which you should be), you don’t have anything on your resume to suggest that you’d know the answers to those questions. In fact, you might have even written a cover letter saying explicitly that you don’t have that experience. What’s happening?

Here’s what’s happening: You’re being interviewed by someone who probably doesn’t take the process very seriously, and you don’t want a job working with people like that. Your interviewer either hasn’t looked at your resume, or doesn’t care enough to pay attention to it (I’ve had more than one interviewer actually tell me that he hadn’t looked at my resume). This is a gigantic red flag because it strongly suggests that the company doesn’t take hiring seriously. Very likely, a decision was made to interview you, and the interview was delegated to someone else who doesn’t have a clue. If this happens, try to be a good sport about it. Be polite. Use the interview to learn what you can about the company, the position, and the industry. Don’t expect a second interview. You’ll have learned something from the interview, and you’ll have invested only a small amount of time. This is not a disaster.

In my experience, that bad scenario doesn’t happen too often. More likely, they’ve made the judgement that you’re a promising candidate because there’s something about your experience that might be valuable. During the interview, they’re going to try to get a more holistic view of who you are, and what you can bring to the job. They’re intrigued by you, and they want to use the interview to get to know you better. Normally, you can be confident that despite your lack of experience, there’s something appealing about your resume — after all, interviewing job candidates is a time-consuming process, and they wouldn’t be investing that time unless there was a reason to think it might pay off.

In this type of interview, your task is to give them an authentic, holistic picture of who you are, in such a way that they’ll remember your strengths after the interview is over. Fortunately, there’s a very good method for accomplishing this goal, and it leads to a much more productive (and enjoyable) interview.

I’ll explain this by contrasting with how interviews are normally conducted. In a solid, but unremarkable, interview, the interviewer asks a series of questions, and the interviewee provides good answers to those questions. When it’s over, the interviewer knows a lot more about the job candidate than before. This often suffices for job-seekers who have a lot of relevant experience, because they can convey that they have all the relevant skills necessary for the job. But this isn’t good enough for those of us who come from a different background and might not be able to check off all those boxes.

Think about someone you know very well — a good friend, perhaps. You don’t just know a lot of facts about your friend; you also know why those facts are true. For example, if your friend really likes dogs, you probably have a sense of why they like dogs so much — maybe they grew up with a very nice dog, or perhaps your friend is an anxious person, and being around a dog is comforting. If your friend is an accountant, you probably know why — maybe your friend is very good with numbers and likes complex, detailed work. This is a big reason you feel that you know your friend very well. You don’t just know a lot about him or her, you know how all those facts relate to each other.

In a job interview, you should try to give your potential employer a similar sense. This is done by relating facts about yourself to other facts. Here’s an example from several interviews I’ve had. I’m almost always asked why I left academia after having been a professor for so long. One of the reasons is that I disliked how isolated I was in my work. I tell them that my old department did not value collaboration, and I always felt that my best work was collaborative. Later in the interview, I’m asked how I would feel about working on a team. I provide an answer like this: “I’d like that very much. As I mentioned, part of the reason I left the university in the first place is because I felt I was stagnating after having worked in isolation for too long. If I could be on a team where I could learn from my colleagues, that would be ideal.”

This answer has a few good qualities. First, it’s absolutely true and totally authentic. Second, it provides a coherent picture of who I am, what my motivations are, and how my job search relates to my dissatisfaction with academia. This is very powerful because it gives the interviewer the (accurate) feeling that they’ve gotten to know me a bit better. And I also find that potential employers remember my interview quite well. This makes sense. After all, it’s easier to remember facts when those facts relate to each other.

Interviews are filled with opportunities to do this. You are a human being with a life, and your experience is not a series of isolated events. To take another example, I was often asked what skills I’d like to learn. When I first transitioned out of academia, my answer was very clear: “As you know, although I’ve done a lot of programming, I’ve never programmed in a professional setting. So I think it’s really important to learn best practices, and the differences between programming as a hobby and programming as an engineer.” Counter-intuitively, this is a good answer because it relates back to one of my weaknesses as a job-seeker. It emphasizes my inexperience, and reinforces the story of how I wound up seeking work in this area (i.e. that I enjoyed programming as a hobby, and am eager to take my skills to the next level). But it’s authentic — that really was my situation, and it would have been foolish for me to try to cover up my inexperience. What I found was that by fitting my weaknesses into the same interconnected structure, I could go a very long way toward allaying the concerns of my potential employer. It told them that my lack of knowledge wasn’t a sign of stupidity or lack of motivation — it was because my experience programming as a hobby wasn’t the right experience for developing those skills.

I’ll end this little post with an observation about people I’ve met who are leaving academia. Every single one of them (and I’ve spoken with many) is wary of the advice given to them about interviewing, networking, structuring a resume, and so on. This has always been because much of that advice makes them feel like they’re not being themselves — that they’re being manipulative or dishonest; and this makes them very nervous and queasy. If you feel this way, then you should examine your feelings very carefully. Sometimes, these feelings are unwarranted. For example, some people feel uncomfortable listing their accomplishments on their resume. If you feel this way, you need to get over it. But in lots of other cases, a feeling of nervousness is a red flag that you should heed.

When you feel uncomfortable with job-seeking advice, there’s one question you should consider: If I were to follow this advice, would I be providing an accurate and authentic picture of who I really am as a human being? If the answer is “no”, then you absolutely should not follow that advice. You should never present an inaccurate or misleading picture of yourself, and you should never feel like you’re playing a role in a story you didn’t write. It’s perfectly fine to feel nervous, but it is not acceptable to feel that you’re not yourself. This is why I hope that the advice I’ve offered on this site will help you in your search, without making you feel like you’re not being true to yourself.


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Transitioning to a Second Job After Leaving Academia: Lessons Learned So Far

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After two years in a really great job, I’ve decided to move on to a new position in a different company. This gives me the chance to reflect a bit on what I learned about the post-academic professional life, and I’d like to share those reflections here.

I was unbelievably fortunate to land a great job with excellent people after leaving academia. Especially considering how little I knew about finding the right job, I was really, really lucky. That experience has taught me quite a lot about what to look for in a job when you’re making this transition.

A little background will put these notes in some context. I landed a job as a software engineer with a fast-growing and dynamic startup, Narrative Science. The business is very unique — we have a system that takes quantitative data of virtually any kind and creates English-language reports about that data which highlight the most important and interesting features. When you consider the deluge of information that needs to be put into a human-readable, understandable form, it’s pretty clear that the potential impact of such a system is enormous.

When I joined, the company had about a dozen engineers. Now, two years later, there are about forty (and the number is growing). As you would guess, the nature of the work has changed a lot during a time of explosive growth like that. It feels like the business has moved out of adolescence and is becoming a grown-up success. I’m really proud to have been a part of that, and it’s very satisfying to be able to be able to point to my own modest contributions to that growth.

My decision to accept a job offer from Narrative Science was guided largely by a single goal — to learn as much as possible, as quickly as possible. Toward that end, I looked for startups that were past their initial rounds of venture capital funding, because that’s typically a growth stage, when the business is evolving very quickly. Additionally, a startup at that stage will never have enough people to do all the work that’s necessary; and that’s useful because it increases the chance that a single person will play multiple roles in the company. This affords everyone the chance to learn an unusually diverse skill set.

I got that experience by working at Narrative Science. Within the first year, I’d touched almost every part of the technology stack in some capacity or other. And just as importantly, I got to see how decisions were being made with respect not only to engineering, but also with respect to the business. Our CEO is unusually forthcoming and accessible, so major business decisions were shared throughout the company. Any dynamic startup that’s attacking tough problems and disrupting industries will also make some mistakes, and it’ll have to spend time figuring out the best strategy in a new market. I saw a lot of those mistakes; I understand many of the reasons for them; and I got to play a part in implementing course corrections when necessary.

But what was most important for my own professional growth was that I landed in a company with smart, generous people. Given how little I knew, I wouldn’t have lasted a week if I’d been working with people who weren’t very generous with their time, and equally patient with a newbie like me. Problems that would have been incredibly frustrating and demoralizing were much more tolerable in a supportive environment.

(By the way, if you’re very cynical and you suspect that I’m white-washing my experience at Narrative Science, go ask my former so-called “colleagues” in academia if I’m afraid to burn bridges. Let’s just say that I’m not known for pulling punches.)

Having benefited from my first post-academic job, I’ve got a much clearer picture of what to look for in a career transition like this. And I’ve also got a good picture of what’s not important. Basically, I think it all comes down to learning and professional development. Particularly if, like me, you’re not terribly young (I was 41 when I resigned my faculty position), you’ll want to be learning every second. In no particular order, here are a few questions you should ask yourself when considering a potential employer for your post-academic career:

  1. Is the company doing something interesting? If you’re going to dive into a new career and make an all-out effort to learn everything you can, you’d better be involved in an endeavor that’s interesting to you. If you’re not, then you’ll never be able to muster the energy you need.
  2. Are your new colleagues pleasant, smart, and generous with their time? As a newbie, you’ll be working with more experienced people, and you will be making a lot of demands on their time. You’ll be asking a million questions every day, and you’ll also be making a lot of mistakes, some of which will require help from your more experienced co-workers to clean up. If your new colleagues aren’t happy to be helpful, you’re going to suffer.
  3. Does the leadership appreciate your background? Every employer in the known universe will say that they value diverse backgrounds and experience among their employees. A small minority actually do. Luckily, this is easy to spot. Just find out about the backgrounds of their current employees. If they’re too similar, then you should think very hard about whether you’d actually be a good fit. Also, think about what they do when they interview you. If they’ve just got a bunch of stock questions that they ask everyone, then you should be skeptical when they say that they like your unique background.
  4. Are they flexible in reassigning people who express a desire to work on something new? A smart employer will jump at the chance to give their employees more experience. But a lot of employers are not smart. It’s easy to find out from people who interview you whether they’ve been given the chance to work on different problems across the company.
  5. Do they put their money where their mouth is? Virtually every employer will profess exactly the right values. But few will spend real, foldable money on keeping to those values. If, for example, they claim that it’s important to them to hire women into technical roles, it’s perfectly fair to ask what concrete steps they’ve taken to do that. And those steps had better include spending some money, because that’s how you tell what they actually value.

What’s not important? Anything that doesn’t help you learn. Realistic salary differences won’t help you learn anything. The weather in the city the business is located in won’t help you learn, either. The name recognition of the company is also not relevant. I started this career transition with the assumption that I should be focused exclusively on learning as much as possible, as quickly as possible. And for the first time in my life, I’ve found that I was right about something.


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Understanding (and avoiding) Shit-Work

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Many academics stay in their academic positions because they see other kinds of work as “shit-work”. They understand that their current positions enable them to spend time working on interesting problems, reading thought-provoking work, discussing ideas with colleagues, and so on. Many other jobs would not give them the time and freedom to engage in interesting activities. Instead, they involve doing “shit-work”. And nobody wants to move from a non-shit-work job to a shit-work job.

Of course, there are plenty of other occupations that are not shit-work. And let’s not forget that a good deal of the day-to-day work of academics is shit-work. But if you’re going to avoid doing more shit-work, you’d better understand exactly what shit-work actually is. That’s what this post is about.

The Dimensions of Shit-Work

There are several distinct factors that determine whether an activity or job is shit-work. If you want to know whether a job is shit-work, you should ask yourself the following questions:

1. Does someone else determine the desired outcome of this work?
2. Is the outcome of this work known in advance?
3. Is there a known (or even mandatory) method of doing this work?
4. Is success fairly certain?
5. Is the work simple?
6. Do you give zero shits about the end-product?

If your answer to all of these questions is “yes”, then you’re doing shit-work. Take, for example, a ditch digger. Someone else tells the ditch digger that they want a ditch of such-such-such length and depth to be dug in a specific location. The outcome is known already: that there will be a ditch there when the work is done. There is a method for digging ditches; this involves shovels. Barring disaster, we can be quite certain that we can successfully dig the ditch. Finally, the work is about as simple as it can be, and it’s very unlikely that the ditch-digger really cares about the ditch. This is why digging ditches is shit-work.

It’s important to bear in mind that even highly skilled occupations requiring a lot of education will meet some of the conditions for being shit-work. For example, even a highly-skilled criminal defense attorney gives up some autonomy insofar as she must seek a (more or less) specific outcome — the desired outcome of her work is that her client received the least possible punishment. In addition, many of her day-to-day activities will be spent following very specific, mandated procedures for various tasks.

Let’s consider another sort of example, which is quite common. These are jobs in which the satisfaction comes mainly from its complexity. An engineer, for example, may be tasked with designing a very complex system; and that task may satisfy almost all the criteria for being shit-work. I’m thinking here of tasks that have well-understood methods, and which are fairly certain to be successful if those methods are followed appropriately. But the sheer complexity of the work can be quite satisfying to an intelligent person with the right temperament. A lot of professional jobs fall into this category.

There’s another important case where all but one criterion for shit-work is met, but the work can be satisfying anyway. These are jobs that might otherwise be unpleasant or unsatisfying, were it not for the fact that the person places a lot of value on the end-product. For example, I’m sure there are plenty of people doing charitable work that’s unpleasant, repetitious, predictable, and so on. But the cause might be good enough that it’s still satisfying. And of course, there are gradations along this scale. I happen to know an extremely smart and talented lawyer with top-flight credentials who could easily be making a fortune on Wall Street, working with a gigantic business, or having wealthy clients. But he takes on a lot of shit-work-ish tasks because it’s important to him to be a public defender. I’ve known people who are in a professional trade that involves a lot of unpleasant work, but they derive great satisfaction from seeing the end-product of their work — whether it’s a road or a building or what-have-you. It’s easy to come up with examples like these.

As jobs become more experimental in nature, various dimensions of shit-work begin to fall away. I’m thinking here of jobs that might involve a lot of exploratory data analysis, for example. A person with this sort of responsibility probably doesn’t know in advance what they’re likely to find in their business’s data. Their job might be to explore their company’s data looking for features that could be of value. Given such an open-ended mandate, it’s less likely that there are well-understood methods or any guarantees of success. And a domain expert with this sort of position will probably have greater autonomy than in a typical job.

Speaking for myself, I really enjoy work that’s open-ended in this way. I like to embark on a project that doesn’t have a clear outcome, and for which success is uncertain. In the long run, I don’t think I’d be very happy doing anything that had a clear and predictable outcome. This work frequently involves a lot of repetitious (and sometimes boring) tasks (e.g. writing computer code that enables some uninteresting function that I need). But this is more than offset by the pleasure I get from exploring a problem that’s not well-understood.

Academics I’ve known have all-too-often looked down their noses at people who do what they would call “shit-work”. But shit-work is a multi-faceted category, and it’s not the same for everyone. Former academics I’ve known have told me that they are satisfied by work entailing trade-offs that are different from what they were used to in their academic lives; many were surprised by what they did and did not enjoy doing once they left academia. I certainly feel this way, myself. Discovering your own definition of “shit-work” is crucial for finding satisfying work outside the ivory tower. More importantly, you have to learn to accept your own definition even if it’s not what the academy says it should be.


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Analytic Philosophy and Software Development

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I’ve always believed, long before I became a software engineer, that my training in philosophy would be helpful for any field like software development or software engineering. But it’s only after having done this for a couple of years that I’m finally starting to understand why it’s so useful to have been trained in analytic philosophy.

There are some common answers that students receive when they ask, “Why should I study philosophy?”. Besides the intrinsic value of philosophy itself, there are several more practical justifications. Philosophy teaches students how to think critically, how to write clearly, and how to analyze complex arguments. These are all useful skills for just about any profession. Although this is all certainly true, I think these skills are not the most important ones that are taught in philosophy.

In short, the most important skill that students can learn from philosophy is the ability to take a vague or ambiguous idea and turn it into something precise — something that can be implemented, communicated, measured, and tested. It’s not about being able to operate within a logical framework; it’s about being able to create an appropriate logical framework in the first place.

Let’s illustrate with an example that will be familiar to the vast majority of us: LinkedIn. Reid Hoffman, the founder of LinkedIn, was a graduate student in philosophy at Stanford before he decided to leave academia and go into the private sector. He was also one of the original partners at PayPal, and has gone on to become an enormously influential venture capitalist. People who are thinking of leaving academia (and philosophy in particular) should check out Hoffman’s writing. He’s done alright for himself, and he’s an excellent writer.

The initial idea behind LinkedIn was that eventually, everyone’s professional identity would have a home somewhere on the internet. LinkedIn was Hoffman’s effort to implement that idea.

Now consider two software developers or engineers: Alice, who comes to LinkedIn very early, before it even has a website, and Bob, who starts work at LinkedIn today. Alice and Bob need to have very different skill sets in order to succeed in their roles. Bob needs to be a good programmer who can understand the massive and complex system that forms the infrastructure of LinkedIn. He needs to be able to add to it, improve it, document it, and work with others to accomplish goals that are most likely driven by fairly well-defined business needs. Logical reasoning, critical thinking, good communication skills, and so on will obviously be very helpful for Bob.

Alice, on the other hand, needs some different skills. Software is necessarily precise and requires exact specifications, but there won’t be any exact specifications of anything when a company like LinkedIn is just getting started. And what specifications there are will probably be revised beyond all recognition as the founders and employees learn more about their business needs. Alice needs to answer questions such as, “What is a person’s professional identity?”, “What activities are valuable to someone who is trying to advance professionally?” and so on. These questions can’t be given vague answers, because at the end of the day, they have to be implemented in a precise way, and an entire infrastructure has to be built to support those answers. In an important way, Alice’s job is far more demanding than Bob’s, because she’s not merely implementing solutions to pre-specified problems; she has to make the problems precise enough to be given practical answers in the first place. Then she’s got to implement them in a way that will enable her to revise those answers later, since they’ll probably not be quite right.

To continue the example, I’d confidently guess that one of Hoffman’s early insights was that a big part of one’s professional identity comprises who you know — in other words, your professional network. Your professional identity not only includes your resume and qualifications, but also anything you write about issues in your profession. These answers are good ones for LinkedIn because they can be implemented in a computer program. They enable us to move from a vague intuition about one’s “professional identity” (whatever that is) to a specifiable solution.

Examples of this transformation of vague idea into precise implementation are easy to find. For instance, people want to stay in touch with their “friends”. But what’s a “friend”? The answer is that a “friend” is someone with whom you’ve mutually agreed to share information. Hence, Facebook. People also want to be able to search for “authoritative” web pages, but what does “authoritative” mean? Sergey Brin and Larry Page’s answer was that an “authoritative” web page is a page that’s been linked-to from many other “authoritative” web pages. That answer can be precisely implemented as Google’s PageRank algorithm.

What all of these incredible success stories have in common is that they’re examples of taking a vague idea and transforming it into something precise. But that skill is very difficult to learn, and it’s exceedingly rare — which is probably why we know the names of people who have it (e.g. Zuckerberg, Hoffman, Brin, and Page). But analytic philosophy is all about taking vague ideas and coming up with precise formulations of them. It’s about creating the right conceptual framework for one’s ideas, and communicating that framework in such a way that it can be unambiguously understood by others.

Not surprisingly, this distinction between operating within a framework and thinking about conceptual frameworks has long been a topic in philosophy (see Carnap’s “Empiricism, Semantics, and Ontology”). The distinction is important, and it’s also highly practical. Once you become aware of the fact that Alice needs skills that go beyond Bob’s, you can avoid making a lot of mistakes. You can focus on the hard work of learning how to build the right framework for answering vague questions. And if you’re running a business, you can avoid the mistaken assumption that Bob and Alice are interchangeable.


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Interview Advice: Be Overtly Authentic

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When I get calls or emails from current or former academics, they’re often concerned with how to handle themselves in an interview. This is perfectly natural, especially when you consider that interviews in an academic setting are very unusual and highly stylized exercises. In an interview for a faculty position, a lot of information can be left unsaid. Everyone knows pretty much everything about the job, what qualities a successful job applicant should have, and so on. This is not true at all for many interviews in the private sector, and so academics have to adjust their thinking about what needs to be said and what doesn’t.

In an academic interview, you don’t really have to broadcast your qualifications or your values. Your qualifications are on your curriculum vita, and it shouldn’t be necessary to say things like “I enjoy teaching”, “I enjoy thinking about problems in my field”, or “I often read about my field when I’m not at work.” So an academic interview is more like an audition — you’re trying to demonstrate that you’ve really got the qualities that everyone knows you should have, which is why you have to do a job talk, perhaps have a little teaching audition, and “talk shop” with potential colleagues.

By and large, interviews in the private sector are not like that (of course, the line isn’t perfectly clear, but I’m speaking in general terms here). In an interview, the potential employer is trying to discover facts about your qualifications, your values, what you know, and what your professional goals are. Your task as a job-seeker is to communicate those features of yourself as clearly, truthfully, and unambiguously as possible.

Paradoxically, the academics I’ve spoken with about the private sector overwhelmingly possess a lot of the qualities that employers are looking for, but they don’t realize that they need to communicate those qualities explicitly. For example, every academic I know who is seeking employment in the private sector has a lot of intellectual curiosity, and places tremendous value in having opportunities to learn. This is a great quality for someone who is seeking work in any kind of fast-changing environment, whether it’s finance, technology, health care, consulting, or what-have-you. Any intelligent employer will want this quality, and most will be frustrated with how difficult it is to find people like that. If an employer doesn’t want someone like this, then that employer is stupid and you don’t want the job, anyway.

So how do you communicate that (e.g.) it’s important for you to have continuing opportunities to learn and develop professionally? The answer may shock you: You should come right out and say, “it’s important to me that I have opportunities to learn and develop professionally.” To take another example, suppose you don’t have experience with technology X, but you want to learn it. Then you should say, “I don’t have experience with technology X, but I want to learn it.” If you value collaboration, you should say, “I value collaboration.” See the pattern?

But how do you convince employers that you’re telling the truth? This is easy, and it has two components, both of which are necessary: (1) you should only say things that are true; and (2) you should provide evidence that those claims are true. For example, many academics I’ve spoken with are frustrated by the lack of collaboration in their academic fields, and that’s one important reason they’re leaving academia. I certainly felt this way. If this describes you, then you could say, “I place a lot of value on collaboration; it’s how I’ve learned the most in the past. In fact, part of the reason I’m making this career change is because I’ve been frustrated by the lack of collaboration in my academic field.” If you’ve got some sort of ongoing side-project, it’s a pretty good bet that your project reveals a lot about you. So you should talk about it.

We can distill this advice down to, “Be overtly authentic.” Being authentic means letting people know what kind of person you really are, not pandering to others’ expectations, and not trying to display values that you don’t actually have. Doing so overtly means not forcing someone else to guess or infer what kind of person you are, and what values you actually hold.

This interview technique (if we even want to call it a “technique”) has several practical benefits. First, it’s easy. Being authentic is easier than being inauthentic because it’s distracting to try to convince someone of something that’s not one-hundred percent true. Second, it wins over your interviewer because people appreciate it when others are straightforward with them. Third, it helps ensure that if you do get the job, you’re a good fit for your employer. After all, the worst possible outcome to a job search isn’t failing to get the job; it’s getting a job that you end up hating. Overt authenticity during an interview will help prevent that from happening.


state of higher education

An Open Letter to the Board of Curators, University of Missouri

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Board of Curators, University of Missouri-Columbia

I am a former Associate Professor in the Department of Philosophy at the University of Missouri-Columbia, and I am writing this letter because you now have the important responsibility of selecting the next president of the statewide university system. You have been given the opportunity to prevent a repeat of recent events. But this will require an honest appraisal of how these crises were allowed to fester over the past several years, beginning with the decision to hire Timothy Wolfe as president of the university system.

Timothy Wolfe was selected to lead the state’s university system in large part because of his experience in business. He was praised as an outsider who could bring sound business and managerial skills to a university system that was caught up in the nation’s economic problems. As an outsider with a business background, it was hoped that his leadership would bring about efficiencies that would benefit everyone in the university system. Furthermore, the system was believed to have a marketing problem, which Wolfe’s experience could be useful in addressing. Indeed, his business background was thought to be so valuable that the fact that he lacked any university experience was not considered to be a significant drawback. By all accounts, he possessed exactly the skills he was touted as having. He is an intelligent, hard-working businessman who set out to bring about those efficiencies.

Many people seem to have been surprised by Wolfe’s inaction and tone-deafness to recent events on campus. The student protesters were rightly appalled by how oblivious Wolfe seemed to be when he was peacefully yet forcefully confronted over systemic racial problems on campus. Yet, I don’t believe that we have any right to be surprised by his and his administration’s ineptitude because his behavior is exactly what we should expect from a man whose entire experience is in the corporate world.

At the heart of Wolfe’s failures is his lack of understanding of university culture and the values held by university faculty and students. The former president simply did not understand concepts of shared governance, the broader role of education in improving the lives of Missouri’s citizens, or the ethical and intellectual standards that are necessary to create a campus in which the university’s broader mission can be achieved. His leadership style was dictatorial, and his actions seemed to be directed exclusively toward achieving the economic efficiencies he was hired to bring about. His recent failures to understand the broader consequences of his decisions were foreshadowed in his earlier decision to close the University of Missouri Press, not to mention his administration’s cutting of health benefits to graduate students – both of which had to be reversed following the predictable reaction of those affected. Clearly, Wolfe did not understand the broader impact of his decisions. And what is more important, he did not understand that those decisions were at odds with the core values of a university. The history of Wolfe’s administration should have allowed us to see in advance that he was ill-equipped to handle recent events.

There is no effective leadership without a solid ethical foundation. No person can have that foundation without also having a deep understanding of the culture and values of the organization he or she is expected to lead. Universities are unique in their culture and values, which is why an effective system-wide president must have a broad range of experience within academic culture.

Thus, I urge you to replace Timothy Wolfe with a person who has a history in higher education, who can demonstrate a sincere and well-informed appreciation for the unique culture and values of the university.

Sincerely,

Zachary Ernst


job security, risk

How to Think About Risk In a Career Transition

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When I announced that I was quitting my academic position and giving up tenure, many people (especially tenured or tenure-track faculty) had the same response: “Why are you taking such a huge risk?”. Giving up the job security of tenure was seen as so risky that many considered walking away from it to be reckless.

I literally never thought of myself as incurring any additional risk by leaving academia. In fact, one of the most important justifications for walking from tenure was that I felt it was an unreasonable risk to remain in my position. Now that it’s been almost two years since I quit, I’m more convinced of this than ever.

Here are some reasons why having tenure in academia is so incredibly risky, and why it’s much safer in the private sector.

You’re held captive in academia

There are a handful of faculty who are superstars, and have the rare privilege of being able to find new positions in other departments whenever they want. For the other 99.9% of us, when you have established yourself in a job, you are effectively stuck there. In the current job market, there are many, many young scholars coming out of graduate school who are truly excellent. It is cheaper to hire them at a junior level, and it’s possible that any one of them may turn out to be a superstar. It’s also less of a commitment for the hiring department because they’ll be hired without tenure. This entails that there are very few jobs for faculty at a senior level. And in my experience at least, when one of those jobs does become available, the hiring department typically has a specific person in mind already, and the hire is basically a fait accompli.

Thus, if you have a “good job” already, this means that your fate is tied to that of your home institution. If the university falls on hard times, if you’re on the losing end of a political battle, if the new dean or department chair doesn’t like you, or if the university’s priorities change unfavorably, there is typically nothing you can do about it. I was in this position, and this is extremely common. Putting yourself in a situation where so much is outside your control and you have no recourse is terribly risky.

If you have tenure, you have an enviable level of job security. But you have no recourse if the job itself deteriorates. Think about it this way: Imagine someone offered you a guaranteed income for the rest of your life, but it would be paid in a volatile currency. You do not have financial security merely because you have a guaranteed income. But many academics, in my opinion, make exactly this mistake when they think of their jobs as offering safety.

Convert one big risk into many small risks

An entrepreneur I know once made the following argument that it’s safer to be in business for yourself than it is to work for someone else, even in a well-established company. If you have a hundred customers, then you can survive if a few of them fire you — the worst that will happen is that your income will drop a little bit. But if you have one boss, you’ll lose your income entirely if that single person fires you. In the former case, you’re taking many small risks; in the latter, you’re taking one big risk. It’s safer in general to take many small risks.

In academia, we take a few big risks. Graduate school is one big risk; going on the academic job market is another; so is going up for tenure. If you manage to navigate this small list of big risks, then you’ll have some measure of job security. But if you fail at any of them, it is very difficult to recover.

To be sure, the private sector can be a rough place. But it’s much more likely to contain many small risks rather than a few big ones. I could certainly be fired from my job, which is a risk; and my job could deteriorate in some way. But I get contacted by recruiters on a regular basis. If I were to lose my job, I could probably get at least two or three interviews for good jobs each week. Of course, not everyone is in this fortunate position. But if you’ve made a few simple strategic decisions, it’s almost certain that you’ll be able to recover much more quickly from a professional setback than you could as an academic.

Think about opportunity cost

The opportunity cost of going to graduate school and pursuing a career in academia is gigantic. But as soon as we start down that path, the opportunity costs start to fade into the background. After a few years, they’re not salient anymore, and we tend not to figure them into our risk calculations. This is a mistake.

Graduate school, job-hunting, getting a tenure-track job, and finally getting tenure make up a very long road. It takes significantly longer than a decade. Even if you get a PhD and immediately land a tenure-track position, you’ve usually got about six or seven years before going up for tenure. When you think about what can be accomplished professionally outside of academia in that length of time, it really puts things in perspective. A well-educated professional can rack up a lot of valuable experience and make a lot of money in that length of time.

And so, when you’re thinking about risk, you should think about the opportunities that are being sacrificed. For example, the risk of failing to get tenure isn’t only that you will lose your job. The risk is that you’ve lost a job when you could have had a successful professional career doing something else, making money the entire time, and gaining professional experience. For some, the risks are worth it. But in my experience, very few academics really take the time to consider those risks in a rational manner.


interviewing, job hunting, resumes

How to Think About Experience

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If there’s one reason why academics don’t make the transition to the private sector, it’s fear. And if there’s one source of all that fear, it’s their perceived lack of experience. Academia requires an arguably unhealthy level of specialization. One unfortunate side-effect of this is that a person’s experience is heavily focused on a single, very small area of specialization. Thus, it’s only natural for for people in this situation to fear that their experience is far too narrow for any “real-world” employment.

The way to get around this fear is by understanding the true value of experience. We often ask about a person’s experience, but we rarely consider the more important question, “why do we care at all about experience?”.

There is absolutely nothing intrinsically important about having relevant work experience. The value of work experience is that it provides evidence. It is evidence that you are capable of doing good work. The same applies to education, job references, work history, credentials, and everything else. Their importance is entirely secondary — they each provide evidence that you’re capable of doing good work.

It is extremely liberating to realize that experience, education, and all the rest are only important because they provide evidence of your abilities. After all, there are many ways to provide evidence of something; and a lack of one kind of evidence can be offset by another. This observation can help you think more productively about how to market yourself to potential employers. For example, here are a few examples from my own experience, or which I’ve seen in successful job applicants who were transitioning out of academia:

  • I’d never given a business presentation. But I’ve given plenty of academic talks, and at least a few talks explaining technical subjects to a non-technical audience. I’ve also logged many, many hours in the classroom, teaching to all different levels of student. In many ways, the skills required in a business presentation are the same.
  • I’d never written an engineering design document. But I’ve written grant proposals. And there are important similarities between these two different kinds of writing.
  • I’ve interviewed (and recommended hiring) people who didn’t know the programming language that they’d be required to use. But in each case, the applicant had demonstrated the ability to quickly learn something at least as complex.
  • I’d never explained technical issues to a non-technical client in a business setting. But I’d done a lot of teaching of technical subjects to students with no technical background. And again, a lot of the same skills are required.

A smart employer will actively try to understand how your skills and academic experience are relevant to their business needs. Of course, even a very smart employer may need some help to understand what was involved in succeeding as an academic; this can be done by structuring your resume in the right way, and by making sure that you’re taking adequate time to explain yourself in a job interview.

In thinking about your experience, don’t trap yourself by thinking too narrowly about what’s relevant. Instead, ask yourself a few simple questions:

  1. What have I accomplished in my academic career? (e.g. teaching, research, presentations, writing, …)
  2. What skills were necessary for me to succeed in those things? (e.g. clarity in writing and speech, the ability to frame an argument concisely)
  3. What other tasks in the private sector require those same skills? (e.g. giving a presentation, writing technical documentation, proposing a solution to a business problem)

It’s surprisingly easy to use your answers to these questions to help you organize your resume, and thereby market yourself to the private sector.


personal, research

Why I Don’t Miss My Academic Research

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When I quit my job as a philosophy professor, many people predicted that I’d miss doing philosophy and thinking about philosophical problems, giving talks, writing papers, and that sort of thing. I don’t.

There are some academics who just love the specific problems that they work on. I’ve known people in philosophy who are almost obsessed with specific problems in ethics, epistemology, metaphysics, and that sort of thing. There’s nothing else that captures their imagination nearly so much as investigating those particular problems. They’re simply not interested in anything else.

In my experience, those people are actually quite unusual. When people say that they “love doing philosophy” (or math, or ancient Greek, or whatever), it’s not the subject that they really love. Rather, the subject is fun and engrossing because of the methods and the skills necessary to investigate it. In my case, I enjoyed working on problems from game theory and formal logic, and I don’t do much of that at all anymore. But I don’t miss it because I’m working on new problems that require a lot of the same skills I used to bring to bear on my academic work. As it turns out, in order to design an algorithm to do something nobody else has done before, and then implement it in a professional setting, you need a lot of the same skills that a logician must have. So software engineering, in my case at least, is a very nice replacement for a large part of my academic work. I think a lot of people who enjoy their academic subject would have the same experience. They’d discover that it’s not the topic that’s so gratifying; it’s the type of problem they’re working on, and how that type of problem gives them the chance to flex their intellectual and creative muscles.

Furthermore, there are a few aspects of my work that are much more satisfying than my academic work. I was always frustrated by the fact that even if my work was published in prestigious journals, it would have very little impact. My work in the private sector has much more impact, both on the startup I work for as well as the clients. That’s very nice, and I could never get that in academia.