The document provides career advice for advancing in a data science career. It recommends focusing on infrastructure skills like AWS and Kubernetes ("get into infrastructure"). It also advises being product-oriented by helping product managers, talking to stakeholders, and taking end-to-end ownership ("be product-oriented"). Additionally, it suggests using the 80/20 rule to focus on the most important 20% of skills that provide 80% of results.

1. Advancing Your
Data Science Career
Alexey Grigorev
Lead Data Scientist at OLX Group
Founder at DataTalks.Club

2. Hello 👋
I’m Alexey.

3. ��
Me when I started in DS
How to make sure my
projects are useful?
Should I work on something
because it’s cool?
How do I get my models in
production?

4. Just knowing machine learning
is not enough*

5. 🔧
Me working
at a startup
How to prioritize because
there’s so much to do and
so little time?
How to build this data/ML
platform?

6. Lead Data Scientist at OLX
Group
Making sure ML projects are
getting adopted by
● Mentoring others
● Scoping projects
Me now

7. Career tips
● Get into infrastructure
● Be product-oriented
● Use the 80/20 rule

8. https://tech.olx.com/detecting-image-duplicates-at-olx-scale-7f59e4b6aef4

9. Mostly engineering work!

10. Mostly engineering work!
Hidden Technical Debt in Machine Learning Systems
https://papers.nips.cc/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf

11. Career tips
● Get into infrastructure
● Be product-oriented
● Use the 80/20 rule

12. Get into infrastructure
● Learn AWS / GCP / Azure / whatever
● Deploy models yourself
● Ask for help
● Learn Kubernetes / Terraform / whatever

13. Career tips
● Get into infrastructure
● Be product-oriented
● Use the 80/20 rule

14. Picture: CRISP-DM
The most
important step

15. Be product-oriented
● Help your PM
● Talk to stakeholders
● Ask “why?”
● Take end-to-end ownership

16. Career tips
● Get into infrastructure
● Be product-oriented
● Use the 80/20 rule
“Full-stack data scientist”

17. Becoming full-stack
Skills
● Product management
● Data analysis
● Data engineering
● Backend engineering
● DevOps
● Setting up wifi in the office
🤯

18. Accuracy
Data

19. Good
Accuracy
Data

20. Good
Accuracy
Data
Great

21. Skill proficiency
Time

22. Great
Skill proficiency
Time

23. Good
Skill proficiency
Time
Great

24. Great
Good
OK
Skill proficiency
Time

25. Skill proficiency
● OK — can use the skill, mostly independently
● Good — can use the skill independently
● Great — expert

26. We don’t need to be
experts in everything!

27. Areas
Depth
Product
Management
Data
Engineering
Machine
Learning
Backend
Engineering
DevOps

28. Areas
Depth
Product
Management
Data
Engineering
Machine
Learning
Backend
Engineering
DevOps
Me a while
ago

29. Areas
Depth
Product
Management
Data
Engineering
Machine
Learning
Backend
Engineering
DevOps

30. Areas
Depth
Product
Management
Data
Engineering
Machine
Learning
Backend
Engineering
DevOps

31. Areas
Depth
Product
Management
Data
Engineering
Machine
Learning
Backend
Engineering
DevOps

32. Areas
Depth
Product
Management
Data
Engineering
Machine
Learning
Backend
Engineering
DevOps

33. Areas
Depth
Product
Management
Data
Engineering
Machine
Learning
Backend
Engineering
DevOps

34. Areas
Depth
Product
Management
Data
Engineering
Machine
Learning
Backend
Engineering
DevOps

35. Areas
Depth
Product
Management
Data
Engineering
Machine
Learning
Backend
Engineering
DevOps
Me now

36. Areas
Depth
Product
Management
Data
Engineering
Machine
Learning
Backend
Engineering
DevOps
How other
DS see me *
* maybe not

37. Areas
Depth
Product
Management
Data
Engineering
Machine
Learning
Backend
Engineering
DevOps
How engineers
see me *
* maybe not

38. Areas
Depth
Product
Management
Data
Engineering
Machine
Learning
Backend
Engineering
DevOps

39. Depth
Breadth
Expert level
Good level

40. Career tips
● Get into infrastructure
● Be product-oriented
● Use the 80/20 rule
“Full-stack data scientist”

41. 80/20 rule
🛠
80% 20%
80%
20%
Effort
Results
📈

42. 80/20 rule
● Break down the role into core areas and skills
● Order the skills by importance
● Pick the most important ones
● Practice practice practice

43. MoSCoW method
● Create a list of features/ideas
● Mark each as “must-have”, “should-have”, “could-have”
● Take one (or two) the most impactful must-have ones
● Make it work with least effort
● Iterate

44. DevOps
● Infrastructure
● Automation
● Monitoring
● Reliability

45. DevOps
● Infrastructure
● Automation
● Monitoring
● Reliability
● AWS
● Kubernetes
● Terraform

46. Help!
● What’s most important?
● What to select?
Find a mentor! (<== Bonus tip!!!)

48. Career tips
● Get into infrastructure
● Be product-oriented
● Use the 80/20 rule
● Find a mentor and join a community
“Full-stack data scientist”

49. @Al_Grigor
agrigorev
DataTalks.Club

50. Backup

51. Product management
● Strategy
● UX & Design
● Communication
● Planning
● Evaluation

52. Product management
● Strategy
● UX & Design
● Communication
● Planning
● Evaluation
● Requirement gathering
● Prioritization
● Stakeholder management

53. Data engineering
● SQL Databases
● NoSQL Databases
● Stream processing
● Batch processing
● Data pipelines

54. Data engineering
● SQL Databases
● NoSQL Databases
● Stream processing
● Batch processing
● Data pipelines
● MySQL
● AWS (S3, Kinesis)
● Spark
● Airflow

55. Backend engineering
● SQL & NoSQL Databases
● CS fundamentals
● Languages and frameworks
● Web services
● Best practices

56. Backend engineering
● SQL & NoSQL Databases
● CS fundamentals
● Languages and frameworks
● Web services
● Best practices
● Python
● Docker
● Tests
● Clean code