What we deliver
Every engagement is scoped toward a working model serving real users, not a PowerPoint deck — from data intake through deployed inference with monitoring, ATO alignment, and a handoff package the receiving team can operate.
- Computer vision — object detection, segmentation, OCR, document understanding, satellite/aerial imagery analysis, medical imaging.
- Natural language processing — classification, named entity recognition, summarization, embeddings-based search, fine-tuned domain models.
- Time-series forecasting — demand, supply, workforce, equipment, budget. Classical (Prophet, ARIMA) through deep (N-BEATS, TFT).
- Anomaly detection — fraud, network intrusion, supply chain deviation, health surveillance, predictive maintenance.
- Tabular prediction — gradient-boosted trees (XGBoost, LightGBM, CatBoost) plus ensembling. The workhorse pattern behind most production federal prediction systems.
- MLOps — model registry, drift monitoring, shadow deployment, A/B testing, rollback paths.
FEDERAL ML DEPLOYMENT COVERAGE
Why Kaggle matters for federal ML
A Kaggle Top 200 ranking means you've consistently beaten 199,800+ other data scientists on held-out test sets across diverse problem types. It is a direct publicly-benchmarked, adversarial test of modeling skill that exists.
For federal work, it matters because many federal ML projects fall short not from lack of data but from modeling choices — wrong architecture, leaked features, overfit validation, no uncertainty quantification. Competition ML training directly addresses these failure modes.
Stack
Modeling
PyTorch, scikit-learn, XGBoost, LightGBM, CatBoost, HuggingFace Transformers, timm, detectron2.
Experiment tracking
MLflow, Weights & Biases.
Serving
TorchServe, ONNX Runtime, Triton Inference Server, FastAPI wrappers.
Feature stores & data
Feast, Parquet + DuckDB, Postgres, Spark for batch feature engineering.
Cloud
AWS GovCloud (SageMaker, EC2 GPU), Azure Government, on-prem CUDA clusters.
Past performance highlights
Production machine learning system
Designed and deployed a live machine learning system at the a federal health agency. Serves real users. Passed full federal security review. In production today. See full past performance →