career by moving to industry. Of
course, it makes a difference wheth-
er you are doing a PhD, already are a
postdoc, or have prior industry expe-
rience. Yet, some salient points have
emerged.
While the low pay, temporary con-
tracts, and statistical odds of achiev-
ing a sustainable academic career are
a great turn-off, the ‘academic dream’
is alive with many who emphasize the
freedom, passion, and personal satis-
faction that comes with research. From
the workshop, I have data suggesting
that most earn less than €40,000 per
year in academia, while I consider it
reasonable to ask for €60,000 or more
as a data scientist with a PhD.
A more critical take is that the struc-
ture of academia demands a narrow-
ing specialization that increases per-
sonal risk (e.g. of later unemployment)
while a move to industry enables the
building up of a broader portfolio of
projects, methods, and technical skills.
I support individuals in this transition
and see that significant career pro-
gress has been achieved after two to
three years, e.g. moving on to more
senior roles.
Most PhD students and postdocs as-
sociate a move to industry – quite
generically – with more security, sta-
bility, and income. Interestingly, many
also expect a broadening of opportu-
nities by achieving real-world impact
through data science product devel-
opment. And yes, often there is an
interface to e.g. customer experience,
business revenue, research and devel-
opment.
As the workshop facilitator, I can offer
additional observations that address
the difference between academia and
industry:
• A significant difference is that ear-
ly-career researchers often work
individually with longer time lines
to publish their results, whereas in-
dustry teams work in shorter cycles,
possibly with daily deployment rou-
tines.
• Perhaps the biggest challenge when
leaving academia after many years
is to get one’s head around the no-
tion of the use of business cases.
Not peer recognition but rather user
feedback and paying customers
matter.
• Industry and startups expect a pro-
26 | NEUROMAG | July 2018
duction-ready data scientist, so set-
ting aside some time for a data sci-
ence project with a demonstrated
and reproducible outcome is valuable
for landing the desired first position.
To empower PhD students and post-
docs in their move to data science and
close any gaps, I have compiled a sim-
ple roadmap. I suggest organizing the
transition in four steps:
1) Exploration of the field: First, get a
feel for and understanding of the role
of the data scientist by utilizing e.g.
courses, hackathons, meetups, and by
interviewing practicing data scientists
and credible recruiters. For you, this
should result in a stop/go decision.
2) Domain orientation: Which field or
industries are you considering? Are
you more interested in computer vi-
sion, big data or natural language un-
derstanding? For health, finance, or
automotive industries? As you consid-
er your options, look for indicators as
to how high or low the entry barrier is.
For example: How new is the product?
How large is the industry? Are start-
ups hiring aggressively and must the
wider industry follow suit (e.g. autono-
mous driving)?
3) Further training (if any): Your inter-
actions will give you an idea if further
training is required for a successful
transition. If in doubt, you can also in-
teract with training providers (e.g. data
science boot camps) to see what gets
their graduates hired in the domain
you are interested in.
4) Career entry: I reckon you want the
learning curve to be steep, so a team
with a good track record in a vibrant
urban environment may be the first
choice. A good track record is indicated
by a growing team, team members
staying for at least two years or more,
a product on the market, and growing
revenues.
There is a way that you can make your
move to data science much more fo-
cused: By working with an industry-
relevant CV from the start. This means
writing a new, second CV that you
take with you for your interactions
and conversations to collect feedback.
Your interlocutors and respondents
may much more easily have some of
the following for you:
1) Suggestions of which domains
might be interesting and accessible for
you.
2) Network contacts that may be in-
terested in your CV.
3) A good idea of what the gap (if any)
is vis-à-vis your preferred job, and
how to close it efficiently.
What do I mean by industry-relevant
CV? A presentation tailored to hiring
managers and recruiters or human re-
sources, making it clear just how you
and your skills are relevant. Such a
document is always individual.
Still, I can offer the following guidance:
• The first page should include your
mission and search statement, a
technical skills overview, and your
last employment.
• The second page includes your edu-
cation, any other employment, and
the transferable skills you bring from
academia to industry.