Nico de Vos *** Debut Ct

San Jose CA *****

acb6cn@r.postjobfree.com

Data Scientist lnkd.in/B_WvU6

Experience

Apr 2008 – Quantitative Research Analyst, Transtrend B.V., Rotterdam, Netherlands (asset management firm focused on

Dec 2013 automated, systematic trading strategies; ranked 14th largest European hedge fund).

Achievements:

Individually created and implemented an automated trading system based on signal processing techniques and statistical

modeling; worked in small teams on improving existing trading systems;

Designed and implemented an econometric model based on company fundamentals and time series regression that

successfully acted as a portfolio filter;

Improved fund money management by overhauling the framework for correlation analysis and non-linear optimization;

Carried large financial responsibility by monitoring daily fund operations and performing day-to-day problem solving;

Developed software for downloading and munging hedge fund databases and subsequent data analysis and visualization.

Education

2003–2009 PhD, Hydrology, Delft University of Technology, Netherlands.

thesis title Computational Intelligence in Rainfall–Runoff Modeling

Performed state-of-the-art research on machine learning, optimization and clustering in hydrological modeling; published

a number of peer-reviewed articles in top hydrology journals; presented work at various international conferences.

Teaching assistant for “Hydrological Modeling” graduate course; lectured and coached MS students.

Visiting scholar to the Hydrology and Water Resources Department of University of Arizona, Tucson, Fall 2006.

1996–2003 MS, Civil Engineering, Delft University of Technology, Netherlands.

thesis title Rainfall–Runoff modeling using Artificial Neural Networks

internship RIZA (Institute for Inland Water Management and Waste Water Treatment), Dordrecht, Netherlands, Summer 2001.

Achievements: overhauled a river contaminant model; coded a data visualization program.

Skills and interests

programming Python + NumPy/pandas/scikit-learn (proficient); MATLAB (proficient); SAS, SCL (proficient); SQL (familiar); C

(familiar); R (familiar)

technical Machine learning (e.g., neural networks, support vector machines, deep learning, ensemble learning, clustering algorithms);

data mining; statistical/econometric modeling; non-linear and population-based optimization algorithms; time series

forecasting; dimension reduction; data visualization; automated trading systems

OS Linux; OpenVMS

competitions Presently a top 250 data scientist on Kaggle.com; 9/25 in 2007 NN3 neural network forecasting competition

general Excellent analytical skills; creative problem-solving mentality; perpetual learner; strong ability to work independently or in

small teams; keen attention to detail; good listener; effective communicator of complex ideas

languages Dutch (native); English (fluent)

Major publications

N.J. de Vos, 2013, “Echo state networks as an alternative to traditional artificial neural networks in rainfall–runoff

modelling”, Hydrology and Earth System Sciences 17, 253–267.

N.J. de Vos, T.H.M. Rientjes, H.V. Gupta, 2010, “Diagnostic evaluation of conceptual rainfall–runoff models using

temporal clustering”, Hydrological Processes 24(20), 2840–2850.

N.J. de Vos, T.H.M. Rientjes, 2008, “Multi-objective training of artificial neural networks for rainfall–runoff modeling”,

Water Resources Research 44, W08434.

N.J. de Vos, T.H.M. Rientjes, 2005, “Constraints of artificial neural networks for rainfall–runoff modelling: Trade-offs

in hydrological state representation and model evaluation”, Hydrology and Earth System Sciences 9(1), 111–126.

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