The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.
I offer a hands-on, statistical best-practices approach to data science. This includes expertise in building and hardening data pipelines, designing statistical experiments, and delivering appropriate, interpretable data analyses.
I have held senior data scientist roles at ServiceNow and Microsoft and, previously, was a senior software developer at Numerix.
My general knowledge of statistical modeling and machine learning is broad and extensive. I have additional expertise in
In the summer of 2018, I started, and continue to contribute to, the PyGCE project. This is an open source framework for deploying python as a service. It allows anyone to run a compute server from their desktop.
From November 2017 to February 2018, I implemented a well-known, bivariate Kalman filter algorithm that can be calibrated without initial state information. Using results from semidefinite programming, I extended this work to multivariate, correlated time series.
During the spring of 2017, I designed a randomized algorithm to detect statistically significant, co-occurring events. This is an important first step in determining causality within an event stream.
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