Frederick Douglas Pearce

Hi, I’m Frederick — Fred for short. I’m an ML and AI engineer with a PhD from MIT who helps organizations answer their most consequential questions with data — building production AI systems that turn complex, hard-to-quantify business challenges into clear, accountable decisions.

That thread runs through almost everything I’ve built professionally. My two most recent projects are good examples. CodeFluent is an open-source VS Code extension and web app that scores how effectively developers collaborate with AI coding assistants, grounded in Anthropic’s AI Fluency Research — which found most developers exhibit only 3 of 11 key fluency behaviors. The Sportswear ESG News Classifier is a fully automated, production AI pipeline — deployed on Google Cloud Run with CI/CD — that monitors 50+ global brands using an LLM-as-judge architecture for ESG classification and sentiment analysis, with ML pre-filters that reduce API costs by 10-20%, and delivers a live, daily-updated scorecard. Both projects are end-to-end: from data collection and model training through deployment and ongoing monitoring.

After a period away as a family caretaker, I’ve returned to data work with a clearer sense of purpose. The AI transformation reshaping the workforce isn’t just a technical challenge — it’s an optimization problem. Are organizations actually becoming more capable, or just faster? Are they adopting AI intentionally and economically, or reactively? I’m currently completing Anthropic’s AI Fluency Framework certification, which pairs naturally with my CodeFluent work and reflects a genuine commitment to this question.

My earlier career spans novel algorithm development, multi-source data synthesis, and quantitative model validation in seismic imaging research at MIT, healthcare data science at Semanticbits supporting CMS’s Quality Payment Program, security anomaly detection and feature engineering at Tripwire, and freelance Data Science work in legal tech and political analytics. I’m based in Portland, OR and open to remote or hybrid roles at startups and mid-size companies where technical depth and clear thinking about AI’s human impact matter.