40. ML
Such description
So much text
Accept
Reject
Moderation queue
Automatic
moderation system
Duplicate
detection
Forbidden
items
Other ML
models
41. ML
Such description
So much text
MP
Automatic
moderation system
Moderation panel
Accept
Reject
Moderators
s3
ES
Duplicate
detection
system
Hashes
Accept
Reject
Moderation queue
42. ML
Such description
So much text
MP
Automatic
moderation system
Moderation panel
Accept
Reject
Moderators
s3
ES
Duplicate
detection
system
Hashes
Accept
Reject
Moderation queue
Index listings & images
43. ML
Such description
So much text
MP
Automatic
moderation system
Moderation panel
Accept
Reject
Moderators
s3
ES
Duplicate
detection
system
Hashes
Accept
Reject
Moderation queue
Detect duplicates
44. ML
Such description
So much text
MP
Automatic
moderation system
Moderation panel
Accept
Reject
Moderators
s3
ES
Duplicate
detection
system
Hashes
Accept
Reject
Moderation queue
Moderate duplicates
45. ML
Such description
So much text
MP
Automatic
moderation system
Moderation panel
Accept
Reject
Moderators
s3
ES
Duplicate
detection
system
Hashes
Accept
Reject
Moderation queue
Collect feedback
57. Plan
● What is OLX
● Data Science at OLX
● Moderation system
● Recommender system
● Way of working
● Expectations from data scientists
58. A project like this is very complex
We need a team (or multiple teams) to make it work: it’s a joined effort of many
people working together
59. Roles in teams
● Product Manager (PM)
● Engineering Manager (EM)
● Software Engineers
○ Backend Engineers (BE)
○ Data Engineers (DE)
○ ML Engineer (MLE)
○ Site Reliability Engineers (SRE)
○ Frontend Engineers (FE)
○ Mobile Engineers
● Product Analysts (PA)
● Data Scientists (DS)
60. Team A
Team B
Team C
Product
PM
PM
PM
Head of
Product
PA
PA
Head of
Analytics
DS
DS
DS
Manager
Data Tech
EM
EM
EM
Head of
Engineering
BE
DE
BE
FE
BE SRE
FE SRE
FE
Matrix structure
61. Feature teams
● A cross-functional team with experts in different areas
● All work together on one feature/product
● All have the same goal!
● Anyone can work on anything, as long as it helps achieve the goal
PA DS DE BE SRE
EM
PM
62. Goal setting
● OKRs, set quarterly
● Great alignment tool: other teams know what you’re doing
● Whatever team is doing, should be in line with their OKRs
Example:
● O
○ Catch more fraudsters
● KRs
○ Precision of model A improves from 30% to 60% while staying at the same recall level
○ Model B is tested in 5 key markets
63. Plan
● What is OLX
● Data Science at OLX
● Moderation system
● Recommender system
● Way of working
● Expectations from data scientists
65. DATA SCIENTIST
DATA SCIENTIST
DATA SCIENTIST
Product
Definition
Data
Processing
Modeling Evaluation Production Customers
Data
Collection
Stages
Focus on modelling and evaluation, a bit
on production
66. DATA SCIENTIST
DATA SCIENTIST
DATA SCIENTIST
Product
Definition
Data
Processing
Modeling Evaluation Production Customers
Data
Collection
Stages