Quantitative Engineer at Continuum Analytics, Inc.

I’m a Quantitative Engineer with over a decade of diverse professional experience in the Financial Markets. I have held roles in Research, Development, Sales, and Trading. Tireless in study and practice, I continue to expand my skill set in all things Finance and Technology, with an emphasis on emerging opportunities and challenges.

I’ve recently joined a visionary team levering the Scientific Python stack to provide the glue that not only binds the modern finance framework, but all domains involved in Data Science.

I believe in a development model for risk technology that combines the rapid prototyping that a high level scripting language provides with the ability to iteratively profile, test, and industrialize code, making it ever faster and more robust. I began my professional career at a startup with this design. We built an engine in C++, then created a domain-specific scripting language for Quants like me to work in. This might seem commonplace now, with examples like Python and Ruby, but having recognized the parallel nature of risk solutions, and looking to exploit the efficiency gains available in a vector-based approach, nothing available at the turn of the millennium suited our needs. Now, with NumPy and SciPy, not only do technologies exist, they are open-source! And it does not stop there. As new compiling technologies like Cython and Numba mature, speeds of interpreted scripts come ever closer to native code, and extensions in C need only target bottlenecks. The Astrophysicist Joshua Bloom recently argued that ‘Python is poised to become the de facto engine for modern science.’ I believe the same is true for finance, marketing, intelligence, and all data-intensive industries,