Sign up for our newsletter and get the latest big data news and analysis.

Interview: Ida Johnsson, Ph.D. Candidate at the Department of Economics at USC

I recently caught up with Ida Johnsson, a Ph.D. Candidate at the Department of Economics at University of Southern California, to discuss how she is actively transitioning to the field of data science. This interview can serve as a compelling example for others wishing to move into the field of data science from other disciplines and explore career opportunities. Ida’s interest is to combine insights from econometrics, statistics and machine learning to develop predictive models, and to use big data to inform decision making. She specializes in time series modeling, theoretical and applied modeling of social and financial networks, as well as modeling of asset price expectations. She has also worked with Bayesian statistics and machine learning, and is particularly interested in how economics and machine learning can be used together to solve problems. Ida has experience working with huge data sets and parallelized computing, implementing data engineering solutions and writing production ready data pipelines.

Daniel D. Gutierrez – Managing Editor and Resident Data Scientist, insideBIGDATA

 

insideBIGDATA: Please give us a brief history of your path thus far toward becoming a data scientist. Specifically, what is your academic background and your current status?

Ida Johnsson: I’ve always liked math and I started out by getting bachelor’s degrees in economics and in mathematics in Sweden. My interest in combining rigorous mathematical methods with conceptual modeling of human behavior led me to a PhD in economics at the University of Southern California. I am currently a Ph.D. candidate and I am going to graduate in May 2018. I am also working as an intern at MD Insider, where I combine a research based approach with programming to develop predictive models for health care and write production-ready data pipelines.

insideBIGDATA: How have you approached your Ph.D. program with respect to engaging data science principles?

Ida Johnsson: During the Ph.D., I quickly became interested in econometrics, as it allows me to use mathematical and game-theoretic reasoning to develop a model and understand it’s properties, and programming and data analysis skills to implement it. During my Ph.D. I have worked on modeling and estimating causal relationships in social networks and financial markets. At USC I am fortunate to coauthor with Prof. Hashem Pesaran, ranked as the top 24th most influential economist according to publications, and named by Thomson Reuters as one of the World’s Most Influential Scientific Minds in 2014. Under his guidance I have learned a lot about how to approach a problem, how to check the robustness of the results and how to bring more clarity to the exposition. All these skills are immensely useful in the field of data science. Along with more traditional econometrics techniques, I am also interested in machine learning, and in particular, how neural networks can be used for predictive modeling of time series. This is one of the topics I am exploring in my thesis.

insideBIGDATA: The industry has seen many people transitioning to the field of data science from other disciplines. Why have you chosen to repurpose your academic background in economics toward a career in data science?

Ida Johnsson: My experience during the Ph.D. has been that there’s a strong focus on academia. While I found purely academic research very rewarding, I also had the desire to work in a more fast-paced environment where I could combine my research and programming skills to analyze complex data sets and derive actionable results. I like collaborating with people from different backgrounds. Working with engineers, I’ve learned a lot about efficient programming and interacting with sales people and people in managerial positions I see how they think about product development, which is a new type of skill to me. All this gives me a different type of knowledge than purely academic research.

insideBIGDATA: What is your vision of a career in data science and how do you intend to apply your skills to find success?

Ida Johnsson: I imagine myself working in interdisciplinary teams and being exposed to new types of questions, data and modeling techniques. My curiosity and desire to learn new tools makes it easy for me to transition into new environments and answer new types of questions. I think one of the most important things the Ph.D. has taught me is how to be a quick and independent learner. I can apply mathematical knowledge, modeling skills and game-theoretical concepts to any new area and learn from the existing literature and other people’s work, and I think this is more valuable than knowing things off the top of my head.

insideBIGDATA: What would your “dream job” be?

Ida Johnsson: My dream job would be a place where I combine cutting-edge computational techniques with rigorous research to derive insights from complex data sets. There is work done in so many interesting areas from AI-powered healthcare solutions to blockchain based technologies in finance. As of now my main areas of interest are healthcare and finance, but these are also the fields where I have most experience. I have learned that there are countless areas I would find fascinating if I only knew more about them, so I’m open minded about my exact future career path.

 

Sign up for the free insideBIGDATA newsletter.

Leave a Comment

*

Resource Links: