This document provides examples of 10 individuals who have transitioned from science careers to data science careers. It includes their previous roles and educational backgrounds in fields like infection biology, communications engineering, genomics, theoretical physics, and more. One quote emphasizes the importance of continual self-improvement of both hard and soft skills over the course of a career. The document also provides information about the growing data science job market and lists resources for supporting the career transition.

1. Science to Data Science
PhDs and MSc moving to startups and industry

2. Ten examples
Dr. Lisa Heße
Dr. Harald Hentschke
Dr. Macarena Beigier Bompadre
From the MPI Infection Biology to Data Scientist with a
knack for ML solutions
From a PhD in Communications Engineering to
optimization algorithms in Data Science
Dr. Dina Deifallah
Dr. Juanjiangmeng Du
From Postdoc in Genomics to ML expert
From theoretical physics to DL expert for visual search
at scale
Dr. Karthick Perumal
From beamline scientist at DESY to ML engineer in
automotive
Dr. Lorenz Kemper
From PhD in Econometrics to Data Scientist
Elizaveta Fotina
From technical assistant at CERN (M.Sc.) to Data
Analytics consultant
From senior research scientist at a university hospital to
freelance Data Scientist
Dr. Setareh Sadjadi
From a PhD in Chemical Process Engineering to Data
Scientist
Dr. Johannes Mosig
From PhD in Mathematics to ML researcher in
Conversational AI

3. #datacareer
“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, or five years of
experience to lead. The determination and
discipline to improve both the hard and soft skills
continually will be the deciding factor.”
Jean-François Gagné

4. What is the market situation?
JF Gagné: Getting the Needed Talent Along the AI Value Chain
(Data for 2020)

5. Free online support
Webinar Slideshare
Medium
publication
>8.5k views of Science to
Data Science workshop
presentation slides over the
past 3 years.
You can download the latest
slides here.
Some noteworthy items
§ The Women & Data
Science scholarship with
>3.7k views
§ Moving from science to
data Science in 6 to 9
months with >1.9k views
and 64% read ratio
§ The industry-ready CV or
Résumé with a 68% read
ratio
You may always register for
the next free webinar at
datascientist.eventbrite.com
Just want to watch?
Go to YouTube.

6. 1600+ practitioners with 2-20 years of experience
in Data Analytics, Data Engineering, Data Science,
Machine Learning, Deep Learning, Natural
Language Processing, Computer Vision, and
MLOps.
Join the community
https://www.theguild.ai

7. The next 20 years
Starting a
#datacareer
The industry-
ready CV
Roadmap to
getting hired
Outline

8. Dr. Macarena Beigier-Bompadre
Data Scientist at Kenjo
Alumna, MPI for Infection Biology
Alexander von Humboldt Foundation
University of Buenos Aires
LinkedIn

9. Dr. Dina Deifallah
Senior Data Science Teacher,
Spiced Academy
PhD in Communications Engineering,
The American University in Cairo
LinkedIn

10. 10
Dr. Chris Armbruster
Director #datacareer, AI Guild
Alumnus, Max Planck Society (MPDL)
European University Institute
Jean Monnet Fellow
Lancaster University, UK
LinkedIn

11. Starting a
#datacareer
1

12. The fastest growing labor market for the
highly qualified
§ Growth of the profession from
2020 to 2030 is by 10x to 20x, up
to 10m data practitioners.
§ Numerate MSc and PhDs from
any discipline are suited.
§ Prior experience with Python (or
R) and SQL is a must-have.
§ Increasing specialization for data
roles and domain expertise.
§ Supported >1000 data talents
directly for a career start.
§ Supported transition right after
the PhD, as well as PhD +1 to +3
years
§ Also successfully handled PhD +7
to +11 years
§ Building the AI Guild platform for
practitioners at datacareer.eu
Key facts Track record

13. …i.e., you have the degree and >12 months
Python experience.
Switch as soon as possible

14. What data role do I seek?
AI Career Pathways: Put Yourself on the Right Track
By WORKERA, a deeplearning.ai company

15. Where to?
CORPORATE STARTUP CONSULTING

16. Which
industry?

17. The industry-ready CV
Get considered by
highlighting your
technical skills
Be invited for
interview by
demonstrating your
business skills
Lead on the
interview by
telling your data
experience
Focus your search
with a
professional
mission

18. Visa, local language, and more…
Migrant students have
the visa advantage.
Job seeker visas are
available for tech
talent.
Some companies now
offer remote hiring.
Data often is in the local
language, and text data
for sure.
The glass ceiling can be
low if you don’t speak
local.

19. Roadmap to
getting hired
2

20. #datacareer
exploration
domain
choice
search v
training
#datacareer
start
Overview

21. q You have >12 months experience with Python or a similar
language.
q Find a practitioner in your network to interview.
q Interview alumni of training providers that have made the
career switch (e.g., online course, bootcamp, university
degree).
q Write up your industry-ready CV.
#datacareer exploration

22. q Explore and understand the use case & the business case
for Data Analytics and Machine Learning.
q Identify preferred domains for your career entry, e.g., by
type of data, data role, and approach (tools and
methods).
q Find out which industry or use case offers the biggest
opportunity (e.g., high demand, low entry barrier).
q Apply to a few suitable career openings and put your
industry-ready CV in front of relevant people.
domain choice

23. q Be proactive with a search and mission statement (and let
people help you).
q Be aware of the trade offs between your preferred
employment versus further training.
q If further training is what you want, consider ramping up
as fast as possible (full-time; practitioner-led).
q An interesting alternative to training is building a domain
portfolio.
search v training

24. q Understand that network contacts, recruiting firms, and
direct applications are equally important to get your CV
placed directly in front of hiring managers.
q Practice how coding challenges and personal interviews
may be different and tougher.
q Look at salary surveys and define your expectation, e.g.,
for Data Science in Berlin €60-75k p.a.
q Make sure you have some criteria to evaluate job offers,
e.g., team, growth opportunity, location, and salary.
#datacareer start

25. The industry-
ready CV
3

26. The industry-ready CV
Get considered by
highlighting your
technical skills
Be invited for
interview by
demonstrating your
business skills
Lead on the
interview by
telling your data
experience
Focus your search
with a
professional
mission

32. 100+ research designs based on statistical inference and data
analytics: I know what type of questions can be answered with which
data.
I have utilized large-scale survey data at the national level (3
countries) covering individual behavior and opinions toward political
parties and regimes.
Additional data included
• Text data: Political party- & elite-level manifestos, parliamentary
speeches, constitutions.
• Global data: Country-level economic indicators, democracy-level,
and democracy-type indicators.
• Socio-demographic data, e.g., household, income, next of kin.
Data experience

33. Data vignette
Research Data Scientist. Using Python & Bash to wrangle
and analyze 70+ TB of simulation data from IPCC models.
Using a new ridge-regression algorithm I developed, I led
a study solving the 18-year-old problem of systematically
quantifying interactions between climate change and the
global carbon cycle.

34. Data project
Model the universe to understand the evolution and
interaction of stars. As part of a team of 15+ researchers, I
performed HPC simulations on clusters (1+ million hours
of computing time), producing and analyzing large-scale
raw data (60 million data points per hour).
• Reduced processing time from 96 hours to 3 hours
leveraging parallelization schemes.
• Implemented mathematical algorithms such as 3D
integrals or PDE solvers.
• Produced publication-quality data visualization, such
as graphs, pictures, and movies.

35. Search and mission statement
I am a Data Analyst with a background in Environmental
Engineering actively looking for a new role in Berlin,
Amsterdam, or another metropolis. I am well versed in
utilizing services and packages (e.g., AWS, Microsoft,
Tableau), and I can build from scratch in Python. Working
with data gives me flow, so I prefer a dynamic startup or
company in an increasingly data-driven industry.

36. Search and mission statement
I am a Data Scientist with a Ph.D. (2020) focused on
regularization theory & optimization. I have 4+ years of
experience in data processing and modeling. I am looking
for a new challenge in Barcelona, Madrid, or Lisbon from
October 2021, preferably in a leading business team
mitigating climate change and reducing the carbon
footprint.

37. Search and mission statement
Data Analyst with a Ph.D. in Computation Physics and big
data experience actively looking for a new role in Hamburg,
Germany, or French-speaking Europe. 3+ years of algorithm
development, high-performance computing, and data
visualization – transferable to finance, logistics, and ML
innovation.

38. HIRING
15+ entry-level roles in Data
Analytics, Data Engineering, and
Data Science.
datacareer.eu/academy

39. The next 20
years
4

40. Choose
your
industry

41. Top 100
AI Startups
in 2021

43. 2021 GERMAN
STARTUP
LANDSCAPE
Contributors & Collaborations
Platforms
Infrastructure
Frameworks
Applications
AI TECHNOLOGY STACK
INDUSTRIES
Water & Waste
Transportation & Mobility
Retail & Commerce
Real Estate
Professional
& Technical Services
Manufacturing
ICT
Hospitality
Healthcare
Government
Finance & Insurance
Energy
Construction
Arts, Entertainment
& Recreation
Agriculture, Forestry
& Fishing
ENTERPRISE INTELLIGENCE
Planning
Forecasting
Discovery
Computer Vision
Computer Linguistics
Computer Audition
ENTERPRISE FUNCTIONS
Marketing
Production
Purchasing & Procurement
R & D
Operations
Sales
IT & Security
HR
Accounting & Finance Customer Service & Support
SCM & Distribution
AI
Startups
in
Germany
2021

46. Organizing
AI
at scale

47. Appendix

48. #datacareer cheat sheet
PRACTITIONERS ALUMNI RECRUITERS “BRING A
FRIEND”
Find a 1st or 2nd degree connection
via e.g.
- LinkedIn, Xing or similar
- Academic alumni networks
- Meetup, conference or similar
Find someone who has successfully
changed career via e.g.
- LinkedIn profile (experience or
certificate)
- Course provider evaluation
platforms like Course Report or
Switch Up
Ask your search engine to find e.g.
- Firms recruiting only for data
roles
- Salary surveys for data roles
Many firms recruit also by ‘bring a
friend’.
You can ‘reverse engineer’ the
strategy by asking your 1st degree
connections to look at your CV – and
pass it on at their company
internally if there is a fit.

49. #datacareer cheat sheet
#USECASE BUSINESS CASE DOMAIN CHOICE ACTIVE SEARCH
Start from your experience, e.g.
- Use and compare the AI of
products you already have
- Use or buy more products, e.g.,
voice systems if you are
interested in NLP
Read up on the business case and
connect to use cases e.g.
- Harvard Business Review or
similar type of resource
- Dedicated online tech
publications
- Publications of companies
evaluating the field in a data-
driven manner
Jump start your domain orientation
by e.g.
- Using market maps for fast
orientation
- Identifying your preferred type of
data like text, numbers, images,
speech etc.
Please remember to include a clear
statement on your CV and LinkedIn
profile that says
- When and where you want to
start
- What domain and/or type of
company you would like to be in
- What you have or bring
especially