Podcast
Transitioning from Software Engineer to Data Science Manager: Search, ML & Leadership
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Transitioning from Software Engineer to Data Science Manager: Search, ML & Leadership
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Episode Overview
How do you move from hands-on software engineering into leading data science teams while staying effective on search and machine learning projects? In this episode Sadat Anwar — a people-centric Data Science Manager and former software engineer fluent in Java, Scala and Python — maps his path from an electronics and informatics background to research in computer vision at Fraunhofer and production search work at OLX.
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Chapter Summary
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- 0:00 - Podcast Introduction
- 1:05 - Episode Overview: From Software Engineer to Data Science Manager
- 1:37 - Early Career & Education: Aspiring Doctor, Electronics Bachelor, Informatics
- 3:12 - Fraunhofer Research Assistant: Computer Vision and Car Dent Detection
- 6:31 - Search Engineering at OLX: First Day Firefighting and Team Onboarding
- 8:42 - Solr Autoscaling: Root Cause, CPU Load, and Scheduled Scaling Fixes
- 10:37 - Decoupling Search from Monolith: Proposal, Implementation, and Experiment
- 14:30 - Tech Stack for Search: Kotlin Services, Python Satellites, and ML Integrations
- 15:13 - First ML Project: Master’s Thesis on Neural Networks for Energy Forecasting
- 18:58 - 20% Time Success: Spellchecker Attempt, Word2Vec, and Recommendation System
- 20:47 - Learning Approach: “Act Before You Think” and Building Practical ML Experience
- 21:58 - Safety Nets for Experimentation: Feature Flags, Backups, Monitoring, Experimentation
- 25:11 - Path to Management: Opportunity, Promotion, and Timing
- 26:28 - Internal Hiring Process: Panel Interviews, Feedback, and Internal Candidate
- 30:25 - People Management Skills: Conflict Resolution, Hiring, and Business Metrics
- 33:46 - The Brag List: Documenting Leadership Evidence for Interviews
- 36:16 - Transition Pain Points: Dopamine Loss, Dropping Hands-On Coding, and Withdrawal
- 40:48 - Managing Momentum: Project Ownership, Milestones, and Team Coordination
- 43:33 - Transition to Data Science Management: Case Study Interview and Motivation
- 44:38 - Trust & Safety Work: Chat Moderation, NLP Challenges, and Fraud Detection
- 48:21 - Managerial Domain Knowledge: When ML/NLP Expertise Helps vs Coordination
- 50:44 - Role Shift: Greater Product Involvement and Stakeholder Influence
- 52:52 - Leveraging EM Experience to Lead Data Science Teams
- 53:56 - Transitioning from Data Engineering/Analytics to Data Science Manager: Advice
- 57:34 - Measuring Managerial Impact: Influence, Business Value, and Team Health Metrics
- 58:36 - Recommended Reading: The Manager’‘s Path and No Rules Rules (Team Culture)
- 1:00:20 - Community Event: Search Meetup — “Bias in AI: How to Measure and Fix It”