Tips for transitioning into tech industries?

A reader writes in:

As an academic philosopher considering leaving the field, I've found your recent alt-ac posts, and the resources posted, very valuable. One possible trajectory I would be interested in learning more about, if this is possible, is the tech trajectory. In other words, suppose one has a doctoral degree in philosophy, but now, all the terrible things about the present considered, one wants to do something quantitative in the tech or adjacent industries. What does that path look like? How does one start to build credentials or the equivalent?

Great question! It's interesting to see that quite a lot of people in my new Philosophers in Industry directory are in tech. I think it would be great to have a guest-post or two on this from people who transitioned into tech fields. Any readers out there interested in contributing a guest post? If so, please just email me at [email protected]. In the meantime, I encourage any readers with experience in tech to post any tips they have in the comment section below!

Comments

[VAP]

I'd add that it would be especially useful to hear from folks who didn't have a background in tech prior to or during their PhD; who didn't work at a tech firm prior to their PhD; and who don't work in areas that seem especially close to that work, such as formal logic, formal epistemology, etc.

(It just feels to me like an overwhelming number of these folks had one or more of the above, and none of these apply to me. I'm just an ethics guy who has never programmed a thing before, and who has only a rudimentary knowledge of anything more complex than MS Word.)

[Seyed Razavi]

I'm a technologist moving the other way so perhaps I can offer a slightly different perspective. I've worked in software engineering / architecture for the past 20 years and hired a lot of people over the years for various roles.

Firstly, the good news is that your background in philosophy, especially if it is well grounded in formal logic, is a real asset. It can often be as good a grounding as some STEM degrees in terms of application. Even if you're more versed in literary traditions within philosophy, clear articulation and the ability to put forth an argument are good abilities to have. Further, your PhD shows you are an independent minded researcher capable of finding solutions to problems on your own.

The next thing to consider is what kind of career in tech you're looking for. There is engineering and its higher abstraction level such as technical / software / systems architecture. There's business analysis and what is called "enterprise" architecture. There's user research, UX and service design or testing. There's also various forms of project and delivery management roles.

All require some effort to acquire the requisite knowledge and experience. Very few firms hire completely junior positions with no prior experience. If you cannot get such a junior position or a paid internship then I'd advise getting some certifications in the appropriate area. Either through classroom based training or online. There are a lot of them out there and it is important to find those that have some credibility within the industry.

Remember the automated software scanning your CV is the first hurdle and it is only looking for words it recognises as aiding your application. So it is not a bad idea to consider the types of jobs advertised and the kinds of things they're looking for before putting together your application. If you're missing key requirements, its necessary to train up unless you have some personal contacts to ease your entry.

Although I've worked with and hired for all the aforementioned roles in private and public sector, my own trajectory is via the engineering path. Here I will say its possible to get web programming jobs with a bit of DIY spirit and a willingness to work long hours for not that much pay. In a couple of years, this can pay off.

However, the route I recommend is to educate yourself in computer science and engineering theory and practice. Then take either a job in the arena of machine learning or systems / back-end engineering. The former requires a strong maths background. The latter requires understanding some more heavyweight programming languages and idioms.

The reason I recommend this route is because it is likely to be more future proof and less likely to have the ceiling in career progression that front end engineers face. Machine learning is very sexy at the moment and well paid of course. But in the mid-career point, other types of systems engineering can be equally rewarding.

One last thing to bear in mind is that technology moves very fast. Entire methodologies and languages rise and fall in cycles the length of Moore's law (~18 months). Some linger for long after but then hit a dead end. Admittedly, there are occasions dead paradigms rise up again (e.g. COBOL before Y2K). But for the most part you either keep up with the changes in the industry or find yourself on a deprecating path to technical support. I think research minded individuals with a philosophy PhD can keep up with the necessary learning. However, the prospect of mastering entire new programming domains in your 30s or 40s when you have family obligations may be daunting. Working from 7am till 11pm on work and private projects to do so may not be possible. So its never too early to keep your mid-point career and life goals in mind.

My recommendation is to consider bringing your background in conceptual analysis (/engineering), logic, argumentation and the like together and pursue an architectural domain within the tech industry. Aiming to make this a goal for your mid-term career as experience in the more hands on areas can be beneficial to acquire first.

This kind of role also favours those with good people skills as a lot of the work is in understanding what makes people tick and how to communicate effectively with them.

Hope this helps!

[Katharine]

Here are some resources I have browsed or used for learning IT basics. I studied for and passed the CompTIA ITF+ certification, which "helps professionals to decide if a career in IT is right for them or to develop a broader understanding of IT": https://www.comptia.org/certifications/it-fundamentals#overview I am also studying for the next certification exams in the sequence, the two courses that comprise the CompTIA A+, which is the entry-level professional certification for computer technicians. I did not pass the first exam on my first try; the level of difficulty was no joke for me, a complete beginner before six months ago.

Most of the resources listed below offer a free week in which to explore the classes offered on the platform.

The Google IT Support Professional Certificate is “designed to take beginner learners to job readiness in under six months.” The five courses are offered through Coursera and cover technical support, computer networking, operating systems, system administration, and IT security.

https://www.coursera.org/professional-certificates/google-it-support
The Google IT Automation with Python Professional Certificate is an intermediate course in which one learns Python, GIT, and IT automation through six courses on Coursera: https://www.coursera.org/professional-certificates/google-it-automation

TestOut offers IT courses with simulation labs in preparation for certification exams like the CompTIA A+. https://www.testout.com/Courses/PC-Pro

CBT Nuggets offers IT courses with simulation labs in preparation for certification exams and for continuing IT education. https://www.cbtnuggets.com/it-training

A third learning platform that I have used and which recommend for online IT courses: Jason Dion, https://diontraining.com/courses/

A fourth learning platform: Udemy. Here’s the course on learning SQL that I plan to take in the future: https://www.udemy.com/course/the-complete-oracle-sql-certification-course/ SQL is the language that is most useful to me in my current role as a Business Intelligence Analyst (although as I noted in my project management guest post, my role is in Agile project management and not actually doing analyst work).

I also recommend the ITIL 4 Foundation certification and training, which covers IT service management and how to integrate IT with the business within an organization. Jason Dion’s training course was great, and the exam voucher is included in the cost of the training: https://itil.diontraining.com/

I looked briefly into coding bootcamps, but I knew about myself that I didn't actually want to become a developer. I also did not want to spend multiple thousands of dollars up front. For me, trying out monthly subscriptions to the above training sites ($29-49 a month) allowed me to explore and learn with a smaller commitment of time and money.

[Samuel Kampa]

During my last year of my PhD, I studied data analysis in Python via DataQuest (https://www.dataquest.io/). On sale, it was $300 for the year, which was a steal given how much I learned. I highly recommend DataQuest for self-motivated newbs. It probably won't give you everything you need to snag a high-powered career in data science right out of the gate, but it could get you that first entry-level position. It can also be a valuable pseudo-credential that sets you apart from the competition for tech-adjacent positions at startups.

Speaking of tech-adjacent startup jobs, I was told by my current supervisor that, were it not for my Python background, I definitely would not have gotten a job as Client Success Specialist. At the time, my Python knowledge was pretty modest. But it was there, and that was enough. Once I proved to myself and to others that I could learn quickly, I transitioned into a more data-heavy role, and now I get to root around in Javascript, too. All that's to say: don't be too proud to take an entry-level job after completing your PhD. As long as there's growth potential, and as long as your employer encourages on-the-job learning, you'll develop a competitive resume quickly. IMO, if your choice is between an entry-level job with growth potential or a coding bootcamp, you should take the job every time.

If you have a background in formal logic, logic programming and query languages (e.g., Prolog, Datalog) may be worth looking into, though they're not as popular in industry as I'd like them to be. React JS is important if you want to go into web development. And regardless of whether you go into data science or development, *learn SQL*! It's a basic prerequisite for a large number of tech jobs.

[Ioana]

Hi, quick line to pitch another idea for your academic philosopher reader: I think one path s/he might like to have a look at AI / machine learning and especially explore the ethics implications. To me, it sounds like a match made in heaven - think of the moral (and other) implications of face recognition etc.
Hope this helps :)
A smile,
Ioana
https://TransitionIntoTech.com

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[Darius]

Thanks guys, you really helped me!
https://www.hardwareforums.com/threads/training-certification-resources.12955/