From Data Analyst
to AI Engineer in 8 Weeks
You understand data, business problems, and stakeholder communication.
Now add the engineering skills to build AI systems, not just analyze data.
You Know Data.
Now Learn to Build.
AI engineers earn 40-60% more than data analysts, but the role feels technically intimidating.
Your SQL and Python are solid, but production software engineering feels like a foreign world.
You're stuck between analyst ceiling and engineer requirements.
From Analysis to Building.
The World-Class AI Engineer Cohort
Data analysts have unique advantages: you understand business problems, stakeholder needs, and data quality issues that pure engineers miss. The gap is software engineering fundamentals and ML implementation. Let's bridge that efficiently.
Engineering Foundation
Add software engineering skills to your data expertise
ML Implementation
Build models and deploy them
Bridge Narrative
Position your analyst background as strength
Meet Your Mentor
My aim has been the same for years: become a world-class AI engineer. Every career move I've made has been measured against that.
I started as a software tester on a $500/month internship in the Netherlands. Taught myself to code, learned to ship real systems, and worked my way to Senior Engineer at GitHub.
Then I left GitHub. I joined an AI research lab as Member of Technical Staff, where I currently build products for secure AI monitoring.
The cohort draws directly from my real experience so you can make progress fast.
I run this special cohort with only a few people because hands-on work with me is what it takes to bring you to become a world-class AI engineer.
Real Results
Vittor
AI Engineer
Built and deployed his portfolio piece, then landed the AI role
"The coaching played a huge part in my success. I focused on AI fundamentals, the certification path, and soft skills like professional writing. Having access to expert guidance gave me confidence during interviews and helped me feel I was on the right path.
I built my own platform (simple but functional) and deployed it on AWS. I used it in my portfolio and showcased it during interviews. The way complex topics were explained, especially the restaurant analogy for AI systems, really stuck with me. Focusing on doing the basics well was absolutely essential."
What You Will Get
8 Weekly Tuesday Sessions
3 hours each for 24 live hours total.
Project Scoping at Kickoff
We set the scope of what you'll ship and the milestones to get there before the live sessions start.
Code Reviews
Reviews of your code from Zen during the cohort.
Lifetime Demo Access
Every architecture demo is recorded and yours to keep.
Demo Day
You present what you built and get feedback from Zen, with a recording you can use in your portfolio.
12 Months Community Access
Included with the cohort.
Business Understanding Is Your Edge
Frequently Asked Questions
What's the main skill gap between data analyst and AI engineer?
The primary gap is software engineering: version control, testing, code organization, APIs, and deployment. Analysts often write scripts; engineers build systems. Secondary gaps include ML implementation (going beyond analysis to building models) and production thinking (reliability, monitoring, scaling). The good news: these are teachable skills with focused practice.
What advantages do data analysts have in AI engineering?
Analysts bring critical skills engineers often lack: understanding messy real-world data, translating business problems into technical requirements, communicating with stakeholders, and knowing which metrics actually matter. Many AI projects fail because engineers build technically impressive systems that don't solve business problems. Analysts turned engineers avoid this trap.
How much time will this take?
You'll spend 3 hours every Tuesday in the live session and roughly 3 hours of async work in between, for 8 weeks. The Tuesday session time is fixed.
I've signed up for cohorts before and dropped out. How is this different?
It probably isn't, and you should hold the money. Most cohort dropouts are people who couldn't articulate what they were shipping when they signed up. That's why the consult exists, and why I turn down most applications. If we get on the call and you can't tell me what you'll have shipped at the end of week 8, I'll point you to the AI Native Engineer community until you can.
I'm not pivoting careers. I want to build a product. Does this still work?
Yes, the cohort works for people shipping their first serious AI system whether the goal is to land a senior role or to launch a product. The shipped system serves both equally well.
Do I need prior AI experience?
You need to be able to code in Python or TypeScript. Complete beginners can follow the classroom they get access to before the cohort sessions to come in well-prepared.
What does it cost?
It's a four-figure investment that we discuss during the 30-minute consult, alongside whether the cohort is the right fit for your project.
Can I do this while working full-time?
Yes, most attendees do. The live session is one Tuesday a week and the async work fits around your existing schedule, as long as you can carve out roughly 6 hours a week.
I accept those who have the highest chance of success.
In the 30-minute call we discuss your goals and whether you are ready for the program.