Ask who I am, what is in this portfolio, how projects connect, or what evidence backs any claim. I pull from the site docs.
Drew Boynton
Software/ML Engineer
I build production-focused ML and data products end to end, from feature pipelines and model inference to APIs, frontend delivery, and automated monitoring.
About Me
I am a software and machine learning engineer focused on building systems that hold up in production, not just in notebooks. My strongest work combines data engineering, model development, and product delivery.
Most of my recent projects are end-to-end pipelines: data ingestion, feature generation, model scoring, API layers, and frontend experiences that expose model outputs in a usable way. I regularly work with Python, TypeScript, BigQuery, Supabase/Postgres, and cloud-based AI tooling.
This portfolio highlights projects where I can demonstrate measurable outcomes, architectural decisions, and operational tradeoffs. If you are evaluating for DS/ML/SWE roles, the flagship systems are the best place to start.
Selected Work
Production-oriented machine learning and data systems are my core focus. These flagship projects represent the strongest examples of modeling, automation, and full-stack delivery.
Flagship Systems
End-to-end systems with measurable outcomes, live interfaces, and automated data pipelines.
NBA Hall of Fame Predictor 🏀
Interactive machine learning model that estimates NBA players' Hall of Fame chances from career production, peak impact, longevity, and award history. Features real-time player lookup and detailed prediction analysis using XGBoost trained on 5,250+ players since 1976. Try entering any NBA player name!
- -5,250+ historical player careers
- -Interactive probability search for any player
- -Feature-level model reasoning on each prediction
PythonXGBoostNext.jsTypeScriptBasketball Analytics
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Search from 5,250+ NBA players • Click the screen above to see behind-the-scenes
Sports Edge: NFL/NBA Betting Analysis 🏈
Machine learning pipeline that computes model spreads and home win probabilities for NFL/NBA games, compares against sportsbook lines, and identifies betting edges. Features real-time odds integration, feature engineering (rest days, form metrics, opponent strength), and automated daily predictions.
- -BigQuery as source of truth + Supabase serving layer
- -Automated daily/weekly prediction pipeline
- -Live portfolio card pulling sportsbook deltas
PythonScikit-learnLightGBMSupabaseNext.jsSports Analytics
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Live NFL/NBA game predictions vs sportsbook lines • Examine the spreads in the screen above to see model analysis
LLM Advisor: Agentic Trading System 🤖
Autonomous trading agent that uses Gemini to analyze market context, adjust mean-reversion thresholds, and execute with guardrails. The premarket bias work from ICTML is being folded into this system so the portfolio tells one trading-advisor story.
- -ICTML premarket bias fold-in in progress
- -Hybrid ML + LLM trading decision stack
- -Risk controls tied to execution rules
PythonGemini APIAlpacaPandasBacktesting
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Work in progress
LLM Advisor rebuild underway
Folding ICTML premarket bias into the live advisor stack, then shipping one dashboard for signals, guardrails, backtests, and execution telemetry.
Premarket bias
Coming into one project surface
LLM thresholds
Coming into one project surface
Execution metrics
Coming into one project surface
ICTML is being folded into this project. Public dashboard polish is in progress while the signal and execution layers consolidate.
Additional Projects
Smaller builds and experiments that highlight breadth across data science, frontend, and algorithmic thinking.
Mancala AI with Game Theory (Try to beat the AI!)
Intelligent Mancala game implementing minimax algorithm with alpha-beta pruning optimization. The AI evaluates game states 5 moves ahead, achieving 70-80% win rate against random opponents with 10x performance improvement through pruning. Features Monte Carlo simulation analysis for strategic validation.
Minimax AlgorithmAlpha-Beta PruningGame Theory
Advanced Data Cluster Sorting
Project for my Advanced Data Science class. This project was a individual effort to sort data into clusters based on their similarity. We used a variety of data structures and algorithms to achieve this.
PythonPandasGaussian Mixture Models
CU Boulder Police Department Heatmap
A simple heatmap of the CU Boulder Police Department data and its most common location occurrences.
ReactNext.jsTypeScriptTailwind CSS
Simple Fitness (Tracking App!)
A native iOS app for tracking strength training and cardio workouts. Built with Swift and CoreData, this was a fun introduction to iOS development and its ecosystem compatibility. This was more a fun project just to learn more about iOS development and its language capabilities.
XcodeSwiftCoreData
