The main task of the group is to use state of the art Machine Learning methods in order to generate financial forecasts from large datasets. As such, the role involves developing novel machine learning techniques and applying them to seek patterns in large, dirty and noisy data sets. A firm understanding of the underlying mathematics will be needed to adapt modelling techniques to fit the problem space. The resulting code base will be at the forefront of machine learning and pushing its boundaries into new and exciting areas. A prerequisite is an extremely strong background in mathematics and computer science. A post-graduate degree in machine learning is desirable.
The successful candidates should be able to understand the theoretical
justifications for the machine learning techniques as well as be able to create
the software to apply the techniques, and where necessary develop those
techniques into new areas. The successful candidate will be someone who is not
just a user of machine learning libraries, but understands and can successfully
use a number of approaches, e.g.: classification, clustering, linear models.
Knowledge and practical experience in application of Bayesian methods, Gaussian
processes and approximate inference is required, any knowledge of kernel
methods, hidden Markov models & linear dynamic systems would be a plus.
What is essential is a hands-on ability to apply mathematical concepts to real
world financial problems, to implement theoretical insights as working code,
and the ability to work independently in a research environment. Numerical
programming is an integral part of the role and experience in an object
oriented language is very desirable.
Educational
background:
-A PhD degree from one of the world’s leading universities or research labs in
one of the following fields: Statistics, Applied Mathematics, Computer Science,
Speech Recognition, Electrical/Electronic Engineering or Physics.
-Up to date knowledge in the field of machine learning and statistical learning
-Deep and thorough understanding of the theoretical justifications of ML
techniques and methods.
Additional skills that they are seeking include:
-Ability to conduct the full spectrum of research on one’s own: from model
design to algorithm development to testing.
-Comfort with some scripting language (Python/Unix shell/etc.) and a numerical
language (R/Matlab).
-C++ / Perl / Bash knowledge is also ideal
-Experience working with real world large datasets