Career Blueprint

Manager→Data Scientist

⏱ 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

🎯

Ready to prove it?

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