All right. So
the next topic would be to think about the fastest growing jobs. So, you know,
you can think of, is that maybe the right avenue for you? Or maybe if it is,
but you're still lacking some skills, [00:17:00]
this is probably, you know, the way forward so that you can upskill while you
are still doing your PhD or postdoc.
So number one,
AI and machine learning specialist. Well, obviously, if you are already working
with large data sets, so you're a quant, then learning to code. It's probably
something that you are already familiar with. The thing is, what kind of code?
Are you learning MATLAB? Are you learning SPSS coding? You know, or is it maybe
already R?
And maybe is
it already Python or something like that? So depending on the languages, there
are more or less complex, but maybe that is now the time to hone in and double
down to learn on machine learning. You know, coding or to understand that and
to look into projects and maybe also, you know, to, you know, take [00:18:00] some courses or even do the same
experience using a different language so that it's not the repetition in the
sense that you want to repeat the experience to confirm the hypothesis, but
instead you could use a new language and redo the experience and the learning
of the new language is the outcome that you have desired.
Number two,
sustainability specialist. Like I said earlier in this episode, about
environmental management tech, but also environmental, social, and governance
standards. So, when you can contribute to these kind of things because you
have, I don't know, a politics or humanities PhD that looks at These aspects
from a bigger timeline or even from, you know, a bird's eye perspective, , or
if you have maybe a biology or [00:19:00]
physicist background where you can, you know, create some models to predict
Thanks for watching.
How
sustainability will look like in the near future or in the far future, then you
will probably be able to contribute in companies that need sustainability
specialists. And then number three, I would say that this is probably the most
common playground for PhD students. So that goes hand in hand with consulting.
It's business
intelligence analysts. So business is something that you probably haven't, you
know, honed in before, but it should be, and it's not rocket science. The
good thing is that most of the business... Concepts, ideas, models or so, are
really easy to grasp. It's
just different, but it's not undoable.
And if you
look into business intelligence, that's probably also something that you can
hone in because, if you have been working with [00:20:00]
data, then looking into business intelligence data is also quite easy. And then
the question really is that you are able to speak with different people from
different units, sales, marketing, legal.
But
essentially, analysts are the graduate entry jobs, and that's probably easiest
for you to jump in. Number four is information security analysts. Well, I would
say this kind of analyst is probably a little bit more advanced in the sense
that you should have studied IT.
I didn't. I
was working with IT security people and it was Way ahead of my understanding of
IT. Number five, fintech engineers. Well, banking, the banking sector is also disrupted digitally. So
fintech is really on the rise. And companies need people who understand the
business side, the banking side, but also the tech underneath.[00:21:00]
So that goes
hand in hand that people who are stemist, who are engineers, who are, who are
prone to find them. So a lot of data, obviously, these are probably then well
received also in the job market. Number six, data analysts and scientists.
Well, there has been, you know, I always laugh when I look at this position
because In 2012, there was an HBR review also on data scientists, the new sexy,
and I would say that that hasn't changed at all
so if you are
A researcher at the moment looking more on qualitative data so far, just
because you like qualitative data or you always find it easier than to learn a
new skill in quantitative research, then this is your sign to build data [00:22:00] literacy, because that is really agnostic,
regardless of the industry, regardless of the roles, data analysts and
scientists are everywhere needed in bigger companies, not so much in the small
and medium companies, obviously, but as soon as you have to aggregate data.
Data analysts
are needed. Number seven, which is more a specialty, robotics engineers. It's a
specialty that is really exciting, but obviously you, you won't need everywhere
a robotics engineer. Number eight, electro technology engineers. Similar to
number seven, I haven't seen them in general.
I would say
that maybe we should also skip number nine, which is agriculture equipment
operators. but number 10 is a gem because it's digital transformation
specialists that are needed in business, and most businesses need them.
And again,
this [00:23:00] is agnostic about in what
industry, in what unit of the company, Every process probably needs to be
digitized and this digital transformation needs people who are able to
understand the digital side of that and to understand the business side of that
and also to understand the human aspects of that.