Codementor - Data Science at OLX
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This document summarizes a presentation about data science at OLX. It discusses OLX's moderation and recommender systems. For moderation, it describes OLX's machine learning models that automatically moderate listings for issues like duplicates, spam, and illegal/NSFW content. Moderators review flagged content. For recommendations, it discusses collaborative filtering and item embeddings to suggest relevant listings to users. It also outlines OLX's team structure, goal setting process, and expectations for data scientists, which include a focus on modeling, evaluation and some production work.
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Codementor - Data Science at OLX
- 1. Data Science at OLX Alexey Grigorev 21 April 2022 Developer Growth Summit 2022
- 2. Plan ● What is OLX ● Data Science at OLX ● Moderation system ● Recommender system ● Way of working ● Expectations from data scientists
- 4. Seller
- 6. Buyer Seller Icons: Bananas, Apple
- 7. Buyer Seller Icons: Bananas, Apple 💶 💶 💶
- 8. Buyer Seller Icons: Bananas, Apple 💶 💶 💶
- 9. Buyer Seller Icons: Bananas, Apple Marketplace
- 10. Buyer Seller Online Marketplace Icons: Bananas, Apple Website 💳
- 12. olxgroup.com
- 14. Plan ● What is OLX ● Data Science at OLX ● Moderation system ● Recommender system ● Way of working ● Expectations from data scientists
- 15. Buyer Seller Online Marketplace Icons: Bananas, Apple Website 💳
- 16. Buyer Seller Online Marketplace Icons: Bananas, Apple Website 💳
- 17. Buyer Seller Online Marketplace Icons: Bananas, Apple Website 💳
- 18. Buyer Seller Online Marketplace Icons: Bananas, Apple Website 💳
- 19. Buyer Seller Online Marketplace Icons: Bananas, Apple Website 💳
- 20. Buyer Seller Online Marketplace Icons: Bananas, Apple Website 💳
- 21. Buyer Seller Online Marketplace Icons: Bananas, Apple Website 💳 ● Smart ranking ● Reducing null-searches ● Query categorization ● Spell checking
- 22. Buyer Seller Online Marketplace Icons: Bananas, Apple Website 💳 ● Smart ranking ● Reducing null-searches ● Query categorization ● Spell checking ● Collaborative filtering ● Item2Vec
- 23. Buyer Seller Online Marketplace Icons: Bananas, Apple Website 💳
- 24. Buyer Seller Online Marketplace Icons: Bananas, Apple Website 💳 ● Image quality ● Listing quality ● Deal prediction ● Price prediction
- 25. Buyer Seller Online Marketplace Icons: Bananas, Apple Website 💳
- 26. Buyer Seller Online Marketplace Icons: Bananas, Apple Website 💳 ● NSFW detection ● Forbidden items ● Fraud detection ● Duplicate detection ● Chat moderation
- 27. Buyer Seller Online Marketplace Icons: Bananas, Apple Website 💳
- 28. Buyer Seller Online Marketplace Icons: Bananas, Apple Website 💳 ● User segmentation ● Bid optimization
- 29. Plan ● What is OLX ● Data Science at OLX ● Moderation system ● Recommender system ● Way of working ● Expectations from data scientists
- 30. Such description. So much text https://olx.com OLX
- 31. Such description. So much text Such description. So much text Such description. So much text https://olx.com OLX
- 34. Problems: ● Illegal items ● NSFW content ● Duplicates ● Spam ● Fraud
- 35. Content moderation Such description So much text
- 36. Content moderation ML Such description So much text Automatic moderation system
- 37. Content moderation ML Such description So much text Accept Reject Automatic moderation system
- 38. Content moderation ML Such description So much text Accept Reject Moderation queue MP Automatic moderation system Moderation panel Moderators
- 39. Content moderation ML Such description So much text Accept Reject Moderation queue MP Automatic moderation system Moderation panel Accept Reject Moderators
- 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
- 47. Plan ● What is OLX ● Data Science at OLX ● Moderation system ● Recommender system ● Way of working ● Expectations from data scientists
- 51. d2 d1 🧦 🧸
- 52. d2 d1 🧦 🧸
- 55. Neural networks (“item2vec”) Icons: srip Neural network Neural network similarity
- 56. https://habr.com/ru/company/avito/blog/491942/ Deep Learning Recommender Systems - Cristian Martinez, Ilia Ivanov (video)
- 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
- 64. Product Definition Data Processing Modeling Evaluation Production Customers Data Collection Stages
- 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
- 67. Interested in working with us? ● We have open positions
- 68. Links OLX: ● OLX tech blog: tech.olx.com ● Jobs at OLX: joinolx.com Me: ● alexeygrigorev.com ● DataTalks.Club ● LinkedIn: agrigorev ● Twitter: @Al_Grigor