Apresentação de como o time de Data Science do Nubank é organizado, o que faz parte do dia a dia de alguém da área e qual o perfil que busca-se para contratar.

1. Nubank Data Science
Past, present and future
12/18/2018

2. Summary
Past
Building the team, the culture
and the technology
Present
Team, projects and
organization
Future
Career, training and hiring

4. Technology
We develop our own
technology exploring
functional programming
and open source projects.

5. Design
We challenge all complexity in
the financial services industry
to always offer our clients a
better experience.

6. Customer
Experience
We make our customer’s life
simpler, helping with everything
that’s in our reach. Your problem,
will always be our problem.

7. Data
Science
We test our ideas and learn
fast by using models and
taking data driven decisions.

8. We are a tech company
that happens to have a
credit card...

9. … this makes all the
difference!

10. Past
THE VISIONARIES

12. Our objective was
simple.

14. Squad Data Science
Centralized
One team handled the demands
Engineering Focused
How do we get this data to work?
Lots of trial and error
Mostly experimenting

15. Technologies used

16. Present
BUILDING A MODEL DRIVEN COMPANY

17. Squad Chapter Data Science
Decentralized
Many teams building models and
tools
Diverse set of skills
Data is available, shipping is easy
Value seeking
We can iterate FAST, always
seeking value

18. Data Scientists / Machine
Learning Engineers
Interns Perennial Models

19. - Credit Risk
- Spending behavior
- Collections
- Chat / Email
- Marketing
- Fraud
- ...
Some examples ...

20. This led to
specialization...

21. Data < something >

22. Make data available
Support business with
data
Data Analysts
Data Engineer
Use data to fuel
decisions
Business Analyst
Data into Predictions
Data Scientist
Minimize execution risk
Machine Learning Engineer

23. Make data available
Support business with
data
Data Analysts
Data Engineer
Use data to fuel
decisions
Business Analyst
Data into Predictions
Data Scientist
Minimize execution risk
Machine Learning Engineer

24. Technologies used

25. DSs / MLEs are spread in
multiple squads over the
company
How are we
organized

26. Business Analysts
Data Scientists / Machine
Learning Engineers
Engineers
Functional
groups

28. Deciding
where to
allocate DSs
and MLEs is
NOT easy
What is the return of a new
model?
VALUE, VALUE, VALUE
How much validation is
enough?
VALIDATION
How long until you have a
solution for a creativity
problem?
TIME
What is the additional return
of an extra team member?
MARGINAL RETURNS
How and when to update a
model?
UPDATES
Do we have the right
people?
PEOPLE, PEOPLE, PEOPLE

29. We estimate value for
all projects but some
human judgment is still
required

30. Future
IT IS ALL ABOUT PEOPLE

31. Career, training and
hiring

33. Career

34. Performance Evaluation
● Modeling
● Coding
● Critical Thinking
● Communication
● Business
● Collaboration
● Chapter Development
● Initiative

35. Scores go
from 1 to 5
Scores
1 Fill the minimum requirements for the job
2 Is solid at the category
3 Performs at squad/small team level
4 Performs well at company level
5
Is recognized by people outside of the
company or did something no other company
did before

36. Training

37. Our focus is learning but
we do not have a
structured process

38. Rituals
● Article reading groups
● Meetup
● Slack Channels
● Chapter meetings
● Decision Sciences Forums

39. Hiring

40. We want
people who
love to learn
Our hiring process is designed
to filter for that. Or at least we
try...

41. The hiring process
CV / Phone interview
Scan the CVs searching for portfolio, projects, past experience,
academic background, competitions (kaggle)
Test / Puzzle
We send you a test or a puzzle so we can validate your experiences
Final phase - More tests
We test your coding, modeling and business skills

42. Principles
We want to be fast to make decisions
There can be some false negatives
We try to be close to people so we
can talk again in some time
The business evolves fast. Some
decisions may change in time