- Frame analytical questions with mission owners and turn them into testable designs.
- Build analyses and models in Python and SQL on cloud-native data platforms hosted in AWS.
- Develop forecasting, classification, risk-scoring, and anomaly-detection models suited to the question and data.
- Design model evaluations with baselines, validation designs, error analysis, documented limits, and verifiable results.
- Build analytical features and datasets from structured, semi-structured, and unstructured data, including document-heavy corpora.
- Use Amazon Textract and foundation models through Amazon Bedrock to extract and analyze text.
- Turn one-off notebooks into repeatable, reviewed analytical pipelines.
- Present findings to decision-makers in plain language with clear visualizations.
- Use AI-assisted analysis, language models, and coding assistants across the analytical lifecycle.
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