I build machine learning systems end to end: architecture, training data, experimentation, orchestration, evaluation, and deployment. Client-facing work below is described in generalized terms to avoid exposing unnecessary implementation details.

Larus Technologies

Data Scientist

Ottawa, Canada

2023 - Present

Build and deploy applied AI systems across enterprise, aviation, defence-adjacent, and sensing programs.

  • Architected hierarchical NLP classification and active-learning workflows for retail taxonomy modernization spanning more than 400M product records.
  • Built RAG pipelines and evaluation tooling for grounded report generation and briefing support.
  • Developed Databricks + MLflow forecasting, pricing, and anomaly-detection workflows for aviation aftermarket decision support.
  • Delivered multimodal sensing and remote-sensing pipelines using EO, IR, GPR, magnetometer, and satellite imagery.

Stack: Python, PyTorch, Transformers, Databricks, MLflow, PySpark, Kubernetes, FastAPI

Outlier AI

Contributor and Reviewer

Remote

Selected engagements

Evaluated frontier AI models in mathematics and physics using research-grade prompts, rubrics, and failure analysis.

  • Created Ph.D.-level evaluation prompts to probe advanced reasoning and edge cases.
  • Designed scoring rubrics focused on correctness, depth of reasoning, and robustness.

Stack: Evaluation design, mathematics, physics, reasoning analysis

University of Ottawa

Ph.D. Researcher, Earth Science

Ottawa, Canada

2019 - 2023

Built open-source ML tooling for earthquake detection, denoising, phase picking, and seismic dataset creation.

  • Created QuakeLabeler for seismic annotation and dataset generation.
  • Built Blockly Earthquake Transformer for configurable seismic model training.
  • Published research on deep learning tools for earthquake detection and seismic noise removal.

Stack: PyTorch, seismic ML, signal processing, tooling, open-source research software