Senior Data Scientist in Webstep AS
David Lara Arango will complete his PhD degree in System Dynamics in 2018. He works as a senior data scientist in Webstep AS.
Looking back, there are so many good memories from my time as a PhD candidate! One of them is definitely those long meetings with my supervisor. Hours and hours went by discussing and testing ideas, results and conclusions (with a joke here and there). I also remember spending time with my friends and colleagues at the PhD office. I think we spent 80% of our time together making jokes related to our topics and 20% having serious discussions. I also remember having the privilege of working with the SD master students, as a teaching assistant and as a conductor of my thesis experiments. I got to meet great people and made very good friends thanks to this experience.
Why System Dynamics?
I wanted to get better at analyzing complex dynamic systems. Understanding such systems work is utterly important to properly deal with multiple problems in a wide variety of areas, such as market price formation, penetration of new technologies, sustainability and many others.
I studied non-cooperative Game theory in dynamic systems. For this I used a combination of System Dynamics, Statistical analysis, Game theory and Experimental economics.
The job in Webstep
I work as a Data Scientist. My main occupations (areas) are:
- Design and implementation of mathematical models to solve complex business problems
- Machine learning product development using Python, R, Octave and others
- Big data analytics using tools like Hadoop and Spark
- Predictive modelling using different methods of Machine Learning, Deep Learning, Markov-Chain methods, Montecarlo simulations and others
- Cloud computing in various platforms such as Amazon AWS, Microsoft Azure and Google Cloud
High analytic capability
System dynamics has given me a very high analytic capability, which allows me to integrate methods across different theoretical areas, as well as understanding complex business problems as dynamical problems composed of non-linearity, delays and feedback processes. System dynamics also allows me to bring a different perspective to a data-driven methodological world (mostly dominated by Machine Learning, Deep Learning and Predictive analysis). This perspective allows me to propose different solutions that are not contemplated in such world as it is.
Advice to new students in system dynamics
System Dynamics is a powerful and interesting methodology. While it is not exempt from limitations (no methodology is), it unlocks the power of defining complex systems in terms of their causal relationships that are able to explain such systems behavior overtime. This is incredibly valuable for policy analysis and overall understanding of relevant problems today. I guess my best advice is, enjoy SD and do your best to get the most out it!