Programming languages
Python for modeling, pipelines, and production ML, SQL for everything data, R for statistical analysis, and JavaScript for interactive apps and tools.
The tools and methods I use to take a problem from question to a model people can trust, and the projects where each one is proven.
Python for modeling, pipelines, and production ML, SQL for everything data, R for statistical analysis, and JavaScript for interactive apps and tools.
Transfer learning and multi-scale fusion with CNNs and Vision Transformers, class-imbalance handling, and Grad-CAM to show what a network is looking at. Coursework spans YOLO detection and LSTM sequence models.
Classification, recommendation, and learning to rank, from item-kNN baselines to LightGBM LambdaRank with multi-source candidate generation and leak-free temporal splits.
Evaluation that keeps models honest: metrics weighted toward costly errors, bootstrap confidence intervals, beyond-accuracy metrics like coverage and novelty, and documented limitations.
Production LLM pipelines and apps with validated structured outputs, offline fallbacks, and token budgets, plus RAG and Model Context Protocol integrations.
Time series, regression, and latent class models, with hypothesis testing and effect sizes that separate what is significant from what only looks suggestive.
Scheduled ETL with quality gates, orchestration, containers, and infrastructure as code, with tests and coverage gates enforced in CI.
Dimensional modeling and SQL across analytical and application workloads, from star schemas in Postgres to object storage on S3.
Charts that make a finding obvious, dashboards for non-technical stakeholders, and accessible front ends people can actually use.
Git and GitHub for every project, with READMEs and architecture decision records written so a teammate or interviewer can follow every trade-off.
Skills are easy to list. This map shows the projects where each one was actually put to work.
| Rewear | Breast cancer | Rainfall | Mile High | Daily Companion | Fashion | Denver | Silicon Valley | |
|---|---|---|---|---|---|---|---|---|
| Deep learning | ||||||||
| Machine learning | ||||||||
| Responsible AI | ||||||||
| LLM systems | ||||||||
| Statistics | ||||||||
| Data engineering | ||||||||
| Visualization |
I'm glad to talk about interpretable ML, LLM systems, or a dataset that deserves better.