I am a Senior Data Scientist with experience applying Python, machine learning, and statistical analysis to operational problems in insurance and venture investing.
I build production-oriented data pipelines using Python, SQL, Postgres, AWS, Docker, and REST APIs. My work focuses on transforming structured records and documents into reliable analytical outputs.
I have developed claims-scoring classifiers, anomaly detection methods, and document-processing pipelines for insurance workflows. I have also built systems that turn submitted claim files into structured fields for adjuster review.
In venture portfolio environments, I create LLM API pipelines for analyzing diligence documents and portfolio records. I document evaluation methodologies to provide investment reviewers with traceable findings.
My technical strengths include machine learning, model evaluation, feature engineering, time-series analysis, error analysis, experimental design, and data engineering practices. I value maintainable pipelines, code review, testing, and clear technical writing.
Build Python services for deal sourcing and portfolio records stored in Postgres. Maintain AWS data pipelines that connect portfolio data to recurring analytical queries for investment functions.
Develop LLM API pipelines to analyze diligence documents for portfolio analysis. Document evaluation methods and create Python summaries that provide investment reviewers with traceable findings from submitted materials.
Developed claims-scoring classifiers in Python for a consumer insurance carrier. Built document-processing pipelines that converted submitted claim files into structured fields for adjuster review.
Created anomaly detection methods to identify unusual claims for manual investigation within insurance claims workflows. Worked with Postgres pipelines to support routing decisions and adjuster review processes.
Bachelor's Degree, Computer Science
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