I love working with data. I spend my days deep in client data, running anything from ad hoc reports and analyses on the most recent table updates, to months long deep-dives iteratively building and testing models for customer behavior, anomaly detection, and other areas. After hours, I meet up with colleagues and friends, and put our data skills to work for non-profit groups and civic organizations, helping them analyze surveys, client information and more.

Coming from an academic research background rich in a variety of approaches for generating, analyzing, and modeling data, I have the skills to learn a new topic quickly and find the best approach to solving a problem. I’ve found that early, thorough research can save a huge amount of time on the back-end.

From years spent excelling at universities doing advanced technical research, to more recently innovating approaches to anomaly detection on large volumes of data, I have been thinking about approaches to analytics for my entire career.

I’m driven by trying to squeeze maximal understanding and insight from new datasets, new approaches and new software. I enjoy arguing about how best to answer a question, but I like, even more, to be proven wrong through a thorough analysis of the data.

When I’m not in front of a computer doing some data cleaning, model building, or coding, I’m usually found outside, cross-country skiing, playing ultimate frisbee or road cycling with friends.


Big Data:

MapReduce, Tableau, Cloudera Hadoop: Hue, Hive, Pig, Impala, Spark

Noisy Data:

R, Python, SAS, Matlab, C, ImageJ, Labview, Fourier processing, 3d modeling, Eigen analysis,

Text Data:

Python, NLP, nltk, automated summarization, regex, sentence tokenizing, named entity recognition

Web data:

Scrapy, API queries, web crawling, xpath