Vinodh Kumar Ravindranath will share learnings from his experience of building high performing teams as CTO at couple of startups (Belong, Bloomreach) as well as Google. He will also share interesting learnings gleaned through data science algorithms analyzing career data.
4. And people don’t join many a time ...
I need to get married
and I need the tag of
“XYZ”
My spouse was supposed
to join this big company
“ABC”. Thats not
happening - so I cannot
risk joining a startup now.
Network
Unreachable!
8. What’s Next?
Skills:
Roles:
Companies:
DNN + Transformers
• Trained on 1B+ career trajectory points
• JDs, Resumes, Public profiles, Industry data
BA –
English,
History from
SJSU
MBA from
Kansas State
Senior BDR
@Autonomy
Manager,
Sales
Enablement
@eFolder
Education Experience
Resume to
Predictive
Model for each
individual
9. What does the career journeys of everyone tell us?
Probability of people with prior “experience of working in
open-ended environment” is 3x of people without such
experience.
1
Open-ended environment = Startups, NGO, PhD
10. ● Look for evidence of working in open-ended
fuzzy setups
● Talk to candidates to evaluate their appetite
for startup environments in exploratory
calls
13. Will they leave?
The Data Science Perspective
The tenure of people who did the
jump from small to big is ~30%
shorter!
14. Startup folks in big companies?
● Create environment to accomodate folks with startup appetite
○ Reduce bureacratic barriers in their work
○ Shield from over-arching process / big company setups
○ Challenging / open-ended charters
16. Python vs Java
Java developers are more in number than
Python in 2014. How would you be hiring a
python developer if you were at that point of
time?
22. What’s Next?
Skills:
Roles:
Companies:
DNN + Transformers
• Trained on 1B+ career trajectory points
• JDs, Resumes, Public profiles, Industry data
BA –
English,
History from
SJSU
MBA from
Kansas State
Senior BDR
@Autonomy
Manager,
Sales
Enablement
@eFolder
Education Experience
Resume to
Predictive
Model for each
individual
Machine Learning Models can come to the rescue!
23. Going for potential can yield you a team of highly passionate, culturally fit
team.
Going for potential can yield you a team of highly passionate, culturally fit and
a “winning team”.
24. #3 Find your biases and get rid of them
Previous Companies, Academic Institute/Degree
Gender
Age, marital status, kids, sexual orientation
Part-time or full-time degree
Work Gap in resume
Disabilities
25. #4 Keep a diversity meter running in your mind
Is my team diverse enough on each of these axes?
And remember diversity is not charity!