ABOUT
Data Scientist & ML Engineer
I spent nearly two decades solving complex problems in technical environments — from live audio engineering to designing enterprise AV systems for 11,000+ users at MillerKnoll. Somewhere along the way, I realized the most interesting problems were the ones hiding in data.
That realization launched a full pivot: a B.S. in Computer Science, an M.S. in Data Science & Analytics (both at Grand Valley State), and a growing portfolio of machine learning, deep learning, and statistical modeling projects that have earned multiple first-place finishes in Kaggle competitions.
Now I'm on MillerKnoll's Lean AI team — a small, high-impact unit leading the company's AI/ML transformation. I build Narrow AI tools that slot directly into business workflows, ship predictive models that inform real decisions, and help drive agentic AI adoption across the org. Same intensity and precision I developed managing mission-critical live events — just pointed at a very different stage.
Audio → Tech → Data Science
Career Pivot
2× First Place
Kaggle Wins
19+
Years in Tech
Lean AI @ MillerKnoll
Current Focus
EXPERIENCE
Associate Data Scientist
MillerKnoll — Lean AI — Holland, MI
- Embedded on the Lean AI team — a dedicated team driving DS/AI/ML adoption across the entire organization
- Designing and shipping Narrow AI integration tools that plug directly into business workflows, making teams faster and sharper
- Building statistical and machine learning predictive models that turn operational data into forward-looking decisions
- Assisting with org-wide agentic AI integration — helping every corner of the business operate more intelligently at scale
Machine Learning Engineer
Wine-AI Startup — Holland, MI
- Leading architectural design of proprietary predictive model for web-based wine industry software
- Built custom API and agentic AI scripts using LangChain and CrewAI with LLM/NLP integration
- Iteratively refined workflows to enhance model scalability and production deployment readiness
Data Science Intern
MillerKnoll — Holland, MI
- Built time series prediction model for North American contract orders — 61% improvement over legacy models
- Applied Dynamic Factor Analysis for dimensionality reduction while maintaining predictive power
- Conducted EDA, stationarity testing, correlation & seasonal decomposition for robust feature selection
M.S. Data Science & Analytics
Grand Valley State University — Allendale, MI
- Focus areas: machine learning, deep learning, statistical modeling, time series analysis
- Multiple 1st and 2nd place finishes in class-wide Kaggle competitions
- Thesis-track research in predictive analytics and applied ML
IT Analyst
MillerKnoll — Holland, MI
- Designed and standardized 300+ global collaboration systems for 11,000+ users worldwide
- Led $5M Chicago flagship showroom AV system design project
- Managed mission-critical live AV technology for Board of Directors and executive leadership events
B.S. Computer Science
Grand Valley State University — Allendale, MI
- Core studies in algorithms, data structures, systems programming, and software engineering
- Foundation in C, Java, Python, and database systems
- Capstone and elective focus on machine learning and data science
Audio Engineer & Studio Manager
Various Employers — Multiple Locations
- 15+ years managing complex technical environments under live, high-pressure conditions
- Developed deep problem-solving instincts, precision under pressure, and cross-functional collaboration
- Origin story — the technical curiosity that eventually led to computer science and data science
ACHIEVEMENTS
1st Place — Kaggle
kNN Recommender System (Birds)
1st Place — Kaggle
Neural Network CITE (ADT Prediction)
2nd Place — Kaggle
Cross-Modal VAE (Biological Prediction)
2nd Place — Kaggle
Wine AI Transformer (Tasting Notes)
61% Model Improvement
Time Series — MillerKnoll DFA
300+ Systems Designed
Global Collaboration — MillerKnoll
APPROACH
Statistical Rigor
Every model starts with the data. Exploratory analysis, assumption checking, and proper validation aren't optional — they're the foundation everything else is built on.
Ship Working Code
A beautiful model that lives in a notebook isn't a solution. I prioritize clean, deployable code — from prototype to production pipeline — because insights don't matter if nobody can use them.
Communicate Clearly
The best analysis in the world is useless if stakeholders can't understand it. I translate complex results into clear, actionable recommendations for technical and non-technical audiences alike.
Iterate Relentlessly
First results are rarely final results. I treat every project as an iterative process — exploring, refining, benchmarking, and improving until the solution genuinely fits the problem.