Summit Founding Partner Albert Lee Profiled in Real World Data Science

June 28, 2023 Summit Consulting

Summit founding partner Albert Lee profiled in Real World Data Science

This article was originally published in Real World Data Science, a project from the Royal Statistical Society, on June 28, 2023. It is reproduced here in its entirety.

‘Living my identity takes courage. It is the same courage necessary to start a new business’

Albert Lee, the founding partner at Summit Consulting, describes his career journey—from mathematics and economics at university to the birth of data science and building a company from scratch.

By Brian Tarran

Hi, Albert. Thank you for sharing your career story with Real World Data Science. Please tell us a little about yourself and your role in data science.
My name is Albert Lee. I’m the founding partner at Summit Consulting, a quantitative and financial consulting firm in Washington, DC. Summit delivers data-driven solutions to help make government effective and society just. I started Summit in 2003, and we recently celebrated our 20th anniversary.

I received my PhD in economics from UCLA in 1999. My professional practice is focused on econometrics—an academic specialty that blends economic theory with statistical practices—and statistical sampling.

What does your job involve?
A large portion of my time is spent running Summit and making decisions about management, personnel, and business development. That said, I am still pretty active in technical topics. I am a testifying expert in econometrics and statistical sampling. Recently, I have been leading a team of data scientists who are reformulating the edit and imputation algorithms for the US Department of Agriculture’s National Agriculture Statistical Service, which collects survey data from US agriculture sectors.

What do you think is your most important skill as a data scientist?
Explaining technical concepts is a big part of my job, and it requires the ability to consume the technical literature and know the concepts well enough that I can explain them to a lay audience (such as lawyers, judges, and program staff).

How has your gender and/or sexual identity factored into your career?
My gender and identity have given me important perspective as a data scientist and an entrepreneur. Living my identity takes courage. It is the same courage necessary to start a new business. From a young age, my identity has conditioned me to be comfortable with differences.

My identity has also taught me to see similarity among differences. Empathy is essential in client services and especially in quantitative consulting, where some of my clients feel disempowered by the complex subject matter.

“The data science field is moving very fast. Every day brings a new algorithm, software program, and hardware innovation. Since data science is a multidisciplinary field, keeping up with it has been challenging.” —Albert Lee

How did you get into data science?
Although I studied mathematics and economics as an undergraduate student and economics as a graduate student, my academic training was very theoretical. I didn’t work with data and computers extensively until my first job outside of academia in the early 2000s. Little did I know that it was the advent of the “big data” revolution.

At Summit we serve mostly federal agencies, who are sitting on decades of administrative data—information they collected as part of their mission but not of research quality. These agencies want to use their administrative data to automate their routine tasks (like predicting which loans will default first) and evaluate program efficacy (determining whether a training program reached its goals). Extracting and analyzing administrative data has been a big part of my career.

When I founded Summit, data science was not a recognized discipline. But as the datasets get larger, decisions about hardware setup, software programs, estimation algorithms, and data virtualization have become increasingly intertwined and interdependent. This really was my first taste of data science as we know it today.

What, or who, first inspired you to become a data scientist?
There are too many people to mention by name. I owe a lot of my career to my first two managers at KPMG, Alan Salzberg and Rick Holt. They taught me how to code and reason quantitatively. And Rob Gould at UCLA has patiently converted a theorist to an empiricist. Once a convert, now a zealot.

What were the hurdles or challenges that you needed to overcome on your route into the profession?
I am an immigrant and a first-generation college graduate. My journey was full of unknowns. Figuring out my academic and professional career has taken a lot of exploration. In this regard, the same exploration that guided my identity also guided my academic and professional journey.

And what are the challenges that you face now that you are working in data science?
The data science field is moving very fast. Every day brings a new algorithm, software program, and hardware innovation. Since data science is a multidisciplinary field, keeping up with it has been challenging. As I progress along my professional journey, striking the right balance between management, hands-on practice, and learning has been difficult as well.

What was your first job in data science, and how does it compare to your current role?
As an entrepreneur, I was given a lot of professional freedom to actualize my career. To a large extent, I have the career that I envisioned. To me, data science lives in the intersection of methods, software, and hardware. I have spent a large part of my career in this intersection.

Of course there are many things that were not part of the original vision, such as running a 100-person organization. My approach has always been intention with openness. By this metric, my current role is not far off from my original vision.

What was the most important thing you learned in your first year on the job?
The ability and the love of learning constantly, regardless of the topic.

What have been your career highlights so far?
The biggest highlight was that on June 15, 2023, Summit celebrated its 20th anniversary! Reformulating the National Agricultural Statistics Service’s edit and imputation systems is also a big deal. And being a testifying expert in some of the most consequential legal cases in the United States was a highlight as well.

What three things are at the top of your current reading/study list?
In recent years, I have been binge-reading Stoic philosophy. I have read most books by Ryan Holiday. His most recent book was Ego Is the Enemy. In between the Stoics, you will find me reading Buddhist meditation literature, including Thich Nhat Hanh’s The Heart of the Buddha’s Teaching. David McCullough’s Truman is also by my bedside.

What advice would you give for anyone wanting to be a data scientist?
Be open and multidisciplinary. Many good ideas in statistics come from other fields, such as economics, medicine, sociology, and education. Computer science enables computational statistics. Having the openness to these topics is key.

What new ideas or developments in the field are you personally most excited about or intrigued by?
Machine learning has transformed statistics both as a consumer and a contributor. It consumes statistics in that it requires cutting-edge statistical techniques and algorithms for its estimation. Machine learning has important applications in many of the statistical sciences.

And what do you think will be the main challenges facing the profession over the next few years?
The proper use of statistics or statistical ethics is an important societal challenge. Machine learning is becoming increasingly sophisticated, and its applications are more broad and pervasive. Machine learning algorithms are making more and more decisions in society, including mortgage loan approvals, residential home prices, and which prisoners receive parole. These are important and weighty decisions. How do we know that these decisions are unbiased and fair?

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