Career Blueprint
Manager→AI/ML Engineer
⏱ 13 months•4 years experience•1 current skills
🧱Phase 1
Foundation
4 months
Skills to Develop
Statistics & probability (hypothesis testing, Bayesian thinking)Python data stack (pandas, NumPy, scikit-learn)SQL at scale (window functions, CTEs, query optimization)Experiment design & A/B testingData visualization & storytelling
Resources
- 📚 An Introduction to Statistical Learning (ISLR)
- 🎓 fast.ai Practical Deep Learning course
- 🔨 Kaggle competitions (complete 3+)
- 🎓 Mode Analytics SQL tutorial
🏁 Milestone
Complete an end-to-end ML project: problem → data → model → evaluation → presentation
⚡Phase 2
Execution
5 months
Skills to Develop
ML pipeline engineering (MLflow, Kubeflow, or similar)Deep learning frameworks (PyTorch or TensorFlow)Feature engineering & feature storesModel deployment & monitoring (drift detection)Causal inference & advanced experimentation
Resources
- 📚 Designing Machine Learning Systems by Chip Huyen
- 🔨 Deploy a model to production with monitoring
- 🎓 Stanford CS229 or CS230 (free lectures)
- 🔨 Build an internal ML tool used by stakeholders
🏁 Milestone
Deploy a model that drives measurable business impact (revenue, efficiency, or user engagement)
👑Phase 3
Authority
4 months
Skills to Develop
ML system architecture at scaleResearch-to-production pipeline ownershipCross-functional influence (product, engineering, executives)Technical mentorship & team buildingPublishing & conference presentations
Resources
- 🔨 Publish a blog post or paper on applied ML
- 👥 Present at a data science meetup or conference
- 🔨 Lead a company-wide data/ML initiative
- 👥 Mentor junior data scientists (2+)
🏁 Milestone
You define the ML strategy — teams come to you for guidance on what's feasible and impactful
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