Executive Data Scientist, Co-founder, Managing Partner at Data Science Central 

May 2012- Present

Author, mathematician, open patent inventor, innovator, and investor. Also co-founder of IoTCentral.io, and founder of AnalyticBridge.com.

Some articles published on DSC include: 

• Data Science Without Mathematics 

• Advanced Machine Learning with Basic Excel 

• State-of-the-Art Machine Learning Automation 

• The Fundamental Statistics Theorem Revisited 

• The Death of the Statistical Tests of Hypotheses 

• 4 Easy Steps to Structure Unstructured Data 

• Best kept secret about linear and logistic regression 

• Difference between Machine Learning, Data Science, AI, Deep Learning 

• 21 data science systems used by Amazon to operate its business 

• 40 Techniques Used by Data Scientists 

• Designing better algorithms: 5 case studies 

• 24 Uses of Statistical Modeling 

• What you won't learn in stats classes

Founder and Managing Partner at Data Shaping Solutions LLC 

Dec. 1999 - Present

Initially a consultancy with clients such as Visa, Microsoft, and eBay, Data Shaping Solutions experienced a new life starting in 2015, becoming a data science think tank, intellectual property and idea foundry, with a focus on automated data science, machine-to-machine communications, robust black-box machine learning algorithms, and statistical / probabilistic number theory research, with applications to Blockchain, Fintech, cryptography, and quantum computing.

Chief Scientist, LookSmart 

April 2010 - March 2012

Keyword and search analytics using machine learning and pattern recognition: 

• Created the list of top commercial keywords that account for 85% of all advertising revenue on Google. 

• Implemented and programmed the Google AdWords API to automatically find millions of new high value / high volume keywords for advertising campaigns (Perl, SOAP, XML) 

• Taxonomy improvement. 

• Click fraud detection (clickjacking and Botnet detection). 

• Website scoring, with scores used to predict chances of conversion or sales, using hidden decision trees / Naïve Bayes and web crawling. 

• Automated bidding for advertiser campaigns based either on keyword or category (run-of-site) bidding. 

• Reverse engineering of keyword pricing algorithms in the context of pay-per-click arbitrage. 

• Creation of multi-million bid keyword lists using extensive web crawling. Identification of metrics to measure the quality of each list (yield or coverage, volume, and keyword average financial value).