N2 Conference 2019 - Workshop on Data Science Careers out of Academia. N2 Network of Doctoral Researchers - Max Planck PhD Net, Leibniz PhD Network, Helmholtz Juniors.

1. Data Science
Career Paths out
of Academia
Chris Armbruster

3. “No matter who you are, self-improvement is one of the most important
and most overlooked attributes of young AI talent. It only takes four
years of experience to become a senior AI researcher, or five years of
experience to lead an entire institute. The determination and discipline
to improve both the hard and soft skills continually will be the deciding
factor in an AI researcher’s career.”
Jean-François Gagné

4. Your outcome today
Guidance: Is a data
career right for me?
A customizable roadmap
for completing the
transition in 6-9 months
Tips and tricks on the
labor market and hiring
managers

5. New economy, new
work?
Career switch to
industry & startups?
The industry-ready
CV
Roadmap to data
careers
Outline

6. • Supported career switchers in getting
interviews that result in more than
one job offer
• Evaluated >500 candidate profiles and
contributed personally to >100
industry-ready CVs
• Published guidance at
medium.com/@chrisarmbruster
Chris Armbruster

7. 10,000 Data Scientists for Europe

8. The AI Guild. A data-centric community led by senior industry experts.

9. Career switch to
industry & startups?
1

14. Key questions
1. What is your
experience with data?
2. Do you use Python or
R?
3. Why do you want to
move to a data
career?
4. Which industry or
field do you want to
join?

15. Roadmap to
data careers
2

16. Exploration
of data
careers
Domain
Orientation
Training
Career
start
Overview

17. Test-drive a few online courses or select some
relevant training books
Find a practitioner in your network to interview
Talk to recruiters and recruiting firms
Interview alumni of training providers that have
made the career switch (e.g. bootcamp, online
course)
Get a sense of your strength and weakness: Data
Analytics v Data Science as a starting point

18. Understand the use case & the business case for
data analytics and data science
Understand where the biggest opportunities are
(e.g. high demand, low entry barrier)
Select preferred domains for your career entry, e.g.
by industry, by type of machine learning
Apply to a few suitable career openings and put
your profile (e.g. LinkedIn, GitHub) in front of
relevant people

19. Do a skills gap analysis for your preferred
employment destination
If any further training is required, consider doing it
full-time and with practitioners
Practice making the use & business case for a
product
Practice how industry skill testing and personal
interviews may be different and tougher

20. Understand that network contacts, recruiting firms,
and direct applications are equally important to get
your profile placed directly in front of hiring
managers
Look at salary surveys and define your expectation,
e.g. for Data Science in Berlin €55-70k p.a.
Make sure you have some criteria to evaluate job
offers, e.g. team, growth opportunity, location, and
salary

21. The industry-
ready CV
3

22. Overview
A summary of the key elements of
an industry-ready CV is available
on Medium
A series of further relevant posts
is also available
Please do your own research, e.g.
on Data Science & Analytics
salaries and job profiles

23. Technical
skills
• Essential for being considered for an
interview at first glance
• It helps you to be confident about the
skills set and to be ready for testing
on any claim made
• The skills set and the ambition (your
next job) must match, and any skills
gap should be closed with relevant
training

28. Transferable
skills
• Your search engine will offer relevant
results for understanding the concept,
which is not to be confused with ‘soft’
skills
• The focus is on skills that are
transferable from one domain (e.g.
academia) to another (e.g. industry),
or across domains (e.g. data literacy)
• In a CV the transferable skills serve
(indirectly) as a call-to-action, i.e.
triggering an interview invite

33. The CV
search &
mission
statement
• Move from applying only to a more
proactive search for opportunities
• State your case at a glance (and stop
wasting time on the cover letter)
• Your mission and search statement
says who you are, what you want to
do, where you are looking, and why
you want to be a Data Analyst or Data
Scientist

34. A collection
of statements
for
inspiration
• Data Science mission & search
statements are collected on Medium
here
• For Data Analytics the statements are
collected on Medium here

35. New economy,
new work?
4

37. Professional
roles
Tech
Tech
Strategy
Tasks: Report what happened
(Descriptive Analytics).
Tools: Dashboarding and
Visualization Tools.
Tasks: Automate decision making
(Predictive Analytics).
Tools: ML, DL.
Data ScientistData Analyst
Technical
Leadership
Data Industry job roles
Expert
Consultant
Team
Management
Consultant
Data Engineer
Tasks: Build, optimize and deploy
machine learning models to
production.
Tools: software engineering and
architecture.
Tasks: Manage Data Pipeline:
collect, clean and pre-process the
data.
Tools: distributed systems,
databases, software engineering.
Machine Learning Engineer
https://towardsdatascience.com/why-you-shouldnt-be-a-data-science-generalist-f69ea37cdd2c
https://www.oreilly.com/ideas/data-engineers-vs-data-scientists
https://hackernoon.com/why-businesses-fail-at-machine-learning-fbff41c4d5db
Natural
Language
Processing
Computer
Vision
Tasks: Build new machine
learning algorithms, find
custom scientific solutions.
Tools: research publications.
Machine Learning
Researcher
...
Information
Retrieval

38. Freelance?

39. Co-found?

40. Organizing
AI for
Scale
…the next twenty years

41. Questions?
More information…
Data Science Career
Paths out of Academia
Chris Armbruster