Between Software Engineering, Data Science, Token Economics and back again, I’ve attempted to deploy technology to make the world more equitable. Any success I’ve had can only be attributed to learning from great people. I'm currently available for consulting/advisory, just get in touch. What follows is a selection of the things I’ve enjoyed doing in reverse chronological order.
2017 ~ A decentralized bank for migrants built on blockchain. The project is complex as it spans multiple domains. From finance to decentralized governance and a quickly evolving regulations. I lead the technical side of the venture, specifically architecting the system and designing the token economy.
2017 ~ I studied Conway's Law, Specifically how organisational structures shape software structures with a focus on microservices and data pipelines. I engaged with multiple organizations to understand how these software structures impacted their organisations and vice versa. This work was supported by and would not have been possible without @mikiobraun (Zalando), @mtritschler (Ebay) and @wvanbergen (Shopify). The conclusion of my work was that it isn’t organisation structures which shape software structure but instead economic incentives which shape both. There was much work to be done here but some of the findings of this research was presented at various conferences.
2016 ~ Providing data sets and algorithms to other data scientists within SoundCloud. My work focused on analysis of discreet events sequential in time (sessions) and their implications on usage. I designed the algorithm based on empirical research, deployed a scalable solution and worked closely with internal customers on how to make use of the data set. Working closely Christoph, I learnt the virtues of rigorous and yet practical statistical methods and how to communicate them with a larger audience.
2014 ~ I was responsible the designing the architecture and scaling the data pipeline from 100 to 50,000 events per second. Led the migration between from RabbitMQ to Kinesis to Kafka while minimising data loss of production systems. Plumping data pipelines is interesting because they reflect an evolving business domain, it’s also damn near impossible. Any success achieved can be attributed to the teachings of @anotherjohng, @omidaladini, @gavinbell, @klorand and countless others. The relationship between infrastructure and business domains that I observed during this period would motivate the research I would pursue in 2017.
2012 ~ Working on a tactical team responsible for making tools that make data available to analysts. We did operational research, load tested software new and old in time when the product was experiencing incredible growth. I engineered and ran simulations based on Markov Chain Monte Carlo to verify the readiness of messaging infrastructure for the newly released “repost” feature. Without the support of @lftherios, it’s possible that this kind of work in particular and data science in general, would not succeeded at SoundCloud.
2010 ~ We built a real time machine learning database. I was responsible for the data pipeline and scaling the analytics storage (Postgres). We were a small company but made big things happen by being clever with in-memory computation. It was here that I established my foundational thinking about machine learning and statistics from the teachings of @nicolaskruchten and @jeremy_p_barnes.
2009 ~ I created a calculation engine for determining the ecological footprint of large corporations. Multinational organisations are subject to different sets of regulations which combined, determine their overall carbon footprint.. The result of our work was a product that helped corporations achieve carbon neutrality.
2008 ~ A mortgage community where I build a search engine for financial products. During the mortgage crisis, there was a general misunderstanding of how financial products were priced. The market was filled with predatory loans and we had an ethical obligation to filter them out. The conversations about the “hard choices” in software engineering that I had with @jpalardy would shape my practice for years to come.
2005 ~ Working on enterprise software development and yearning for a better way.