Summary

Adept with the entire data science process, from data pipelines to operationalization of production level machine learning systems.

Analyst with strong mathematical ability and experience with messy, terabyte scale datasets.

Skilled at turning data into actionable business insight and decisions.

Experience

Implement machine learning algorithms on usage data for the Microsoft Azure cloud to provide insights on growth, customer segmentation, fraud detection and resource allocation.

Built probabilistic model to predict customer growth on compute clusters, improving cluster provisioning decisions and decreasing overhead.

Wrote modular, extensible framework in Python for modeling capacity allocation and developed simulations to improve hardware configuration, customer allocation and more nuanced overhead.

Improved latency and interactivity of reports for Microsoft VPs from 3 days to 4 hours using a combination of SQL, d3.js and crossfilter.

Performed statistical data analysis on military veteran and family attendance and event satisfaction data for the Yellow Ribbon Rehabilitation Program (YRRP) of the DoD.

Created Microsoft Access database for YRRP, significantly improving data collection and analysis.

Member of the team that developed a face recognition algorithm using deep belief networks which performed favorably against popular open source software using MATLAB and C.

Tested the efficacy of commercial and open source face recognition algorithms with regards to various image quality metrics using Python.

Created standard image sets for baseline measurement of algorithm efficiency and performance.