Applying advanced data science techniques to network analytics in support of effort to reduce false positives in cybersecurity application. Responsible for data ingest (combination of web-scraping, APIs, text files and CSV). Processing has included Latent Dirichelet Allocation and K-means clustering (unsupervised machine learning methods), Natural Language Process (NLP) using Natural Language Toolkit (NLTK), and logistic regression (machine learning method for categorization). Have implemented graph based structures of data in neo4j and more recently, networkx. Processing pipelines supported by Python (including a full set of data science tools - most especially pandas/numpy and scikit-learn), R, general Linux tools such as Perl and Emacs. Much of my time is spent on 'feature engineering' to allow for meaningful application of machine learning tools. Core data elements are persisted in MySQL.Have contributed to enhancements to python module ipwhois. Seeking other opportunities to support interesting open source projects.Migrating some of my workflows from scripts to Jupyter Notebooks - great for self-documenting, reproducible analyses.Enjoy Python a lot, attended Pycon2018 in Cleveland. I write __iter__ often, __enter__ and __exit__ not as much :)

Contract management for security firm offering portal access to datasets relating to worldwide Improvised Explosive Device (IED) incidents. Offices in Arlington, VA and Shrivenham, UK.

Contract management and pricing for IT services firm.

Providing consulting support drawing on my areas of expertise. Majority of my work has been at Lockheed Martin Headquarters, automating a number of processes using Visual Basic for Applications (VBA) (because that was one of the policy-compliant tools available there).

(Dates shown include a break in service from 1995 to 1999.)Hired as a software engineer writing assembly language code in 1982. Worked my way up through the Radar Physics Group, overseeing programs to build and deliver state-of-the-art MkV instrumentation radars, the Enhanced Detection and Processing (EDAP) product for field upgrade of US Navy SPS-67 radars, and the Ariane V re-entry breakup measurement program in support of the Centre National d'études Spatiales (CNES – the French space agency).