Advancing Prediction and Growing

📍 Refocusing on U.S. Projects and Prediction-Oriented Models

In 2009, due to family needs and other factors, I stepped back from international travel and withdrew from overseas projects to concentrate on U.S.-based work. I used this inflection point to serve a smaller set of organizations more deeply, integrating organizational logic into my models and refining predictive methods to improve forecast accuracy and project success rates.

Some colleagues initially questioned this shift, but I explained that my expertise— methods, processes, and technologies of data analysis—was always meant to help projects succeed, especially organization-wide initiatives. In fact, improving predictive accuracy and project success is a common aim across civilizations’ methodological advances—often reaching beyond the narrow modern-science paradigm—and a profoundly meaningful pursuit.

🧠 4E-Infused Modeling: Organizational Logic, Latents, and Faster ML

By incorporating organizational operations, latent variables, including faith and spiritual factors, and a 4E-based statistical-learning framework, my models often achieved greater accuracy and faster deployment than conventional approaches.

In 2007–2008, I tested several risk models for a major U.S. bank with promising results, reinforcing my resolve to focus on enterprise applications. I resigned from adjunct teaching positions to dedicate myself fully to non-academic projects.

Yet, as data science and AI began to surge, collaborations with Stanford, USC, and others actually increased—and during this period I continued to supervise a few doctoral projects on spiritual capital.

🚀 “Data Scientist” in the Wild: Ingram Micro, Davenport, and Early Momentum

My renewed focus happened to coincide with the rise of data science and machine learning, creating unexpected opportunities—and a platform to advocate for positive social impact in these fields.

My first major engagement was with INGRAM MICRO, a Fortune 100 company, where a former Disney CIO led a data-driven transformation. I led a small team and, over three months, developed a strategy for using analytics to guide business transformation, laying a foundation for the company’s shift.

Executives there admired Thomas H. Davenport’s Competing on Analytics, and from then on I followed his work closely and later collaborated with him.

🔍 Search, Fintech, and Program Evaluation: From Shopzilla to Toyota

After INGRAM MICRO, I consulted for Shopzilla, enhancing search relevance through predictive modeling. There, a Silicon Valley consultant insisted that I use the title “data scientist”—one of my earliest public uses of the term, which was still uncommon at the time.

I then joined a fintech startup founded by former Google and Capital One executives as a machine learning specialist, where I built the company’s first credit-scoring production model, which outperformed a prior consulting build.

Additional projects followed: a money-transfer firm’s data transformation, a Toyota customer 360 model, and, for USAID, an analysis of U.S. compensation programs for civilian casualties in Iraq. The latter was a unique learning experience; I declined in-country fieldwork for safety reasons, and the project did not continue.

📈 Growing with the Field: The Data Scientist Narrative

These projects enabled me to learn quickly, validate models with real-world data, and grow alongside data science itself. When I was first hired as a data scientist in 2010, very few had even heard the title.

Meanwhile, Thomas H. Davenport’s writings were propelling the field—his 2012 Harvard Business Review article on the “sexiest job” became a defining reference.

After a talk at Harvard, Davenport and I compared notes: he was a Harvard sociology Ph.D., I a Stanford sociology Ph.D.—and it seemed rare for sociologists to have entered data science so deeply.

🌐 RMDS Community and Joining IBM (2013–2019)

In 2009, with friends and colleagues, I founded the Research Methods and Data Science (RMDS) community. Without formal promotion, it grew into a globally recognized network with tens of thousands of members.

The Southern California chapter partnered with the City of Los Angeles, Disney, USC, and others to host events on smart cities, smart governance, data management, and weather prediction—often cited as catalysts for the region’s growth in data science.

After I joined IBM in 2013 as a big-data scientist, the RMDS community expanded even faster, opening new doors for collaborations and publications.

📚 Capital Debates, Values, and Public Interest AI (2014–2016)

In 2014, Thomas Piketty’s Capital in the Twenty-First Century reignited debates relevant to my 4CAPITAL research. While working in London in 2016 with IBM, I engaged more deeply in discussions about using data science and AI for societal good.

Given my advocacy and city-level collaborations, for example with Los Angeles and Chicago, some organizations began to associate my name with the idea that data science should remain centered on public benefit.

In 2016, I traveled to China for IBM data and machine learning training, visited my hometown in Jiangxi, and furthered my faith-related explorations with a visit to Salt Lake City.

🛠️ IBM Projects: Watson, Spark, Weather, IoT, and NASA JPL

At IBM, I contributed to projects integrating R, SPSS, and Watson; building machine learning process management on Apache Spark; creating weather-data business applications; developing advanced data science and AI training and certification; managing risk-prediction workflows; and advancing IoT data intelligence.

We brought Watson Studio and related machine learning technologies to major organizations, including Saudi Aramco, Farmers Insurance, and NASA JPL, and collaborated with universities such as Harvard, the University of California, and Caltech.

I also supported European banks and companies in London and Sweden. A NASA JPL collaboration on hurricane-density prediction using machine learning later received the American Meteorological Society’s Banner I. Miller Award and other innovation honors.