Ayotomiwa Oladotun

From marketing to models

My career began in content marketing. I was good at planning growth strategies, but I quickly realized that planning wasn't enough. I wanted to build, test, and own the "backend of marketing" itself. I couldn't just *suggest* a strategy; I needed to be able to *build* the system that would execute it.

The insight

This curiosity led me down a path of deep technical learning. I figured I couldn't truly build effective growth systems without understanding user behavior on a data-driven level. How could I build a system to engage users if I didn't know *why* they engaged in the first place? This insight became my new focus.

My work today

My work now stands on two pillars that directly answer that challenge:

1. Understanding Behavior (Machine Learning): I build machine learning models to analyze user behavior, find patterns, and predict needs. This is the "why." My projects in this area range from predictive price models to user segmentation and rarity analyzers.
2. Automating Engagement (AI & Workflows): Once we have the "why," I build the "how." I am an expert in automation platforms like n8n, using AI to build intelligent, end-to-end systems that deliver personalized engagement at scale, all while maximizing efficiency.

What this means for you

I am the bridge between the growth team and the engineering team. I don't just analyze data and present a report; I build the automated workflow that acts on it. I don't just suggest a personalization strategy; I build the machine learning model that powers it. I turn growth ideas into working, data-driven systems.