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systems

You’ve found my new blog, DataScientistInABox.com.  Thank you for visiting, hopefully I will make it worth your time.  I plan to bring my unique perspective from combining data science, systems thinking, and decision analysis.

MyTimeline

After completing a degree in physics, like many physicists of the time, I entered the world of software and systems development.  My early work included making (pseudo) random number generators with the right mix of performance and “randomness”, business intelligence tools for data warehouses, social media sites, on-line business-to-business systems, and Internet-based project collaboration software.  Employers and clients included Proctor & Gamble, National Car Rental, Perot Systems, and Red Sky Interactive.

In September 2001 an opportunity at John Deere presented itself; I was employed at Deere until April 2015 when I left to join H2O.ai.

At Deere I developed software products in the areas of B2B exchanges, food traceability, logistics and coordination, and geospatial data management and insights.

In 2010 and 2011 I started as a Systems Design and Management fellow at MIT and started working in Deere’s central research group, respectfully.  My research at MIT focused on optimal decision making under uncertainty, applied to complex systems development efforts.  My research at Deere had the same flavor until I became very focused on the impacts of algorithms, artificial intelligence, and machine learning would have on the impact of people, companies, and society at large.

Deciding that talking about “the rise of the machines” was no substitute for doing actual work, since 2012 data science has been my sole professional focus.  I’ve worked in the areas of consumer research, soil physics and machine optimization (combining probabilistic physics models with sensor measurements for model development & prediction), logistics optimization meshed with predictive models, customer churn/adoption, market segmentation, transaction fraud detection and prevention, the Internet of Things, applied machine learning, data mining, and on.

The more I work closely in these technical areas, the more I have been able to bring a systems thinking perspective the table.  It is that which I plan to present here.

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