Podcast
From Software Engineer to VP of Machine Learning: Stakeholder Buy-In, Rapid POCs and Full-Stack Skills
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From Software Engineer to VP of Machine Learning: Stakeholder Buy-In, Rapid POCs and Full-Stack Skills
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Episode Overview
How do you move from a hands-on software engineer to a VP of Machine Learning while getting stakeholders to say “yes,” delivering rapid POCs, and building the full-stack skills teams need? In this episode Jack Blandin walks through that transition. Jack began as a Software Engineer in 2015, shifted into Data Science and Machine Learning in 2017, and has held ML and leadership roles at Fi, Wayfair, Trunk Club, and GoHealth—managing teams of 2 to 15. He’s now VP of Data Science & Machine Learning at Fi, finishing a.
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Chapter Summary
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- 0:00 - Podcast Introduction
- 0:13 - Guest Overview: Jack’s career arc from software engineer to VP of ML
- 1:04 - Career Pivot: Transition from full-stack engineering to data science
- 2:41 - Early Leadership: Informal management and promotion at GoHealth
- 4:47 - Rapid Advancement: Reflections on moving from IC to manager
- 6:53 - Leadership Learning: Trial-and-error development of soft skills
- 9:01 - Problem Framing: Technical context and product-level understanding
- 11:33 - Reputation Management: Building respect, trust, and influence
- 15:25 - Stakeholder Communication: Speaking marketing language (CAC, KPIs)
- 17:22 - ML Project Complexity: Resource needs and cross-functional buy-in
- 20:48 - Selling ML: Fast POCs and demos to generate stakeholder support
- 23:18 - Demo Design: Visualizations and user-centric proof-of-concepts
- 26:15 - Risk Communication: Explaining model trade-offs without raw accuracy
- 28:17 - Rapid Prototyping Tools: Gradio, Streamlit, and lightweight demos
- 28:46 - Baseline First: Start with heuristics and manual processes before ML
- 31:03 - Hypothesis Validation: Quick experiments to test product assumptions
- 34:09 - Actionability Over Accuracy: Churn model lesson on usable insights
- 36:44 - Outcome Focus: Avoiding technical tunnel vision on ML tuning
- 37:34 - Data Generative Process: Treating data as a shadow of reality
- 40:37 - Domain Immersion: Customer empathy through product usage
- 44:35 - Full-Stack ML: Importance of software engineering for production ML
- 47:58 - Content & Community: Daily LinkedIn posts and where to follow Jack
- 50:37 - New Venture: Reimagining hiring and recruiting for ML/data roles
- 53:02 - Episode Wrap-Up and Final Remarks