This document discusses the transition from academia to industry. It begins by providing context about the author's background, including obtaining a PhD in applied mathematics. It then discusses some challenges, such as losing a job unexpectedly and having to find a new one quickly. Several key points are made, including that many PhD graduates end up working outside of academia, and the importance of constantly updating skills like programming and using tools like GitHub. The document provides advice on aspects of the job search process, such as common job titles for those with math and science backgrounds, and preparing for and navigating interviews. Overall, it aims to provide guidance and reassurance for academics seeking non-academic careers.

1. Transitioning From
Academia to Industry

2. What this is not
I’m not encouraging leaving
academia.
I would have stayed if possible.
I don’t have all the answers (or even
close to majority).
I’m not going to speak on the cons of
academia or answer questions that
don’t seem appropriate.

3. About Me
Got PhD in applied mathematics
August 2014.
Had three job offers for postdoctoral
work.
Chose the wrong one.
Got job at Booz Allen Hamilton
(pending clearance) as Operations
Research Analyst.

5. Beginnings
Not all jobs are assured.
I lost my job unexpectedly (kind of)
and had one month to find a new
one!
“If I don’t find a job in academia I’ll
just get an industry job”

6. Lets get a job! (things to
know)
8000 PhD’s in the UK are bartenders!!
80% of PhD’s end up working outside
of academia.
17% of STEM field PhDs get a
professorship within 3 yrs. of
graduation [1].
Reproductive number for STEM
professors around 7.8 [1].
[1] Larson, R. et al, Systems research and behavioral science
2014.

7. Always apply for positions!!

8. To do: constantly…
Update CV AND Resume!!! (most
jobs wont ask for CV).
Got to job fairs and had out resume
and business card.
Updating git repositories and
website!
Do kaggle or intensive python
coding!

9. Lots of jobs (need to know
what to look for)

10. Problems
Having a
degree doesn’t
guarantee a
job.
Position you’re
applying for.
Skills required
for job.

11. Position you are qualified
for
Anything with “analyst” in the job title.
“Data scientist”
This is literally what you did for 5+
years!
Need convincing…
Most programming positions (know your
skills).
Consulting (many “prestigious” firms hire
non MBA people these days…)

12. Data + Science =
Research!

13. So how to get one of those
60,000 jobs on LinkedIn?
Git
Python/Ruby/Perl
C++/Fortran/C#
Website
Public code

14. Git
Almost every job posting asks for
your github page!
In your first interview they will ask
about it.
Use the git tutorial (its really great)!
https://try.github.io/levels/1/challenge
s/1

15. Python
Take a character string and reverse the order of the
characters (you have two minutes).
Char = ‘hello world’
Char_reverse = Char[::-1]
What’s the difference between an array and a
dictionary?
Dic stores key-value pairs, arrays values are
referenced by an index.
What’s the difference between a tuple and a list?
Tuple is immutable (can’t be changed, referenced by
pointers)!

16. Website (Githubpages)
Spend a day and create a webpage using
Github pages.
Great way to get used to git and have a
forever website!
Code:
https://github.com/ajbaird/ajbaird.github.io.g
it
Tutorial: https://24ways.org/2013/get-
started-with-github-pages/

17. Website: Jekyll, Markdown

18. Process
Initial phone screening (top 20%).
Second longer phone interview (top
10%).
On-site interview (Top 3-8).
Remember: They may not hire
anyone.
PhD in applied math got me in the
door.

19. Interview Process
Seems like a mess to
me.
Often times I felt like
they knew before
hand they wouldn’t
hire me.
People interviewing
you don’t know the
difference between
Masters and PhD.

20. First Interview
With company hiring manager.
Need to have an elevator talk of
research ready.
Need to use as many buzzwords as
possible.
I used python to do…, I developed
Matlab code that…, I Analyzed data
to understand…,

21. LinkedIn is your friend!

22. Job interviews: like comps
with no prep time.
You need to know what they will ask
(Glassdoor, research on LinkedIn…)
I spent a day or two prepping.
You need to be ready to code!
I had three programming tests (two
timed on the spot).
https://github.com/ajbaird/ipsos_tutorial

23. Onsite interview
This went two different ways for me:
Discuss my role:
Here’s what we do, how do you fit?
Does this seem interesting to you?
Where do you see yourself here in 5 years?
3hr in person test
Practical: Here’s a computer do stuff in python.
Software engineering: Design code structure for a
library (on whiteboard).
Math: State the binomial distribution. What is the
probability 3 of 10 coin flips are heads?

24. Anecdotal Evidence
John works at Deloit consulting after pure math
postdoc.
Physicist PhD from Cornell happily works at
Boston Consulting Group.
Simi happily works at Amazon.
Robert works at CDC in Australia!
So it happens!

25. Conclusions
Always look for and ponder new opportunities.
Never sacrifice your well being on a job (there are many
many more out there!)
Try and become as competent as possible in one largely
used programing language.
I applied for 100+ jobs:
20 stage 1 interviews
10 stage 2
2 onsite
1 job!
Prepare to be unemployed.