1234 Mountain Dr
Salt Lake City, UT 84888 NATHAN K. KARREN
(888) 123-4567
Provo, UT Brig...
of 1


Published on: Mar 3, 2016

Transcripts - Nathan_K_Karren_LinkedInVersion

  • 1. 1234 Mountain Dr Salt Lake City, UT 84888 NATHAN K. KARREN (888) 123-4567 nathanaddress@email.com EDUCATION Provo, UT Brigham Young University 2006 – 2011 B.S. Mathematics, GPA: 3.6 B.A. Economics, GPA: 3.9, with Minor in Physics  Awarded academic scholarship through eight semesters for maintaining ~3.8 GPA  Graduate Coursework: Measure Theoretic Probability, Econometrics, Econ Macro, Econ Micro  Undergraduate Coursework: Java, Calculus, Lin-Algebra, Classical and Quantum Physics, Financial Economics, Econometrics, Macro, Micro, Statistical theory, Real Analysis, Abstract Algebra, Complex Analysis, ODE, PDE GRE (2014): 165/170 Verbal, 165/170 Quantitative; (95% and 90% respective quantiles) ACTUARIAL EXAMS Exam P/1 – Sep 2011 Exam FM/2 – Oct 2011 Exam MFE/3F – Nov 2011 EMPLOYMENT Financial Analyst, Corporate Finance Zions Bancorporation Oct 2013 – Jun 2014  Programmed, validated, and maintained 80 models projecting firm’s welfare under hypothetical scenarios  Procured and spliced data from myriad SQL databases and company sources; prepped for analytics in SAS  Extensively documented models’ logic and caveats for annual submission (CCAR) to the Federal Reserve Cared for family member Eugene, OR Mar 2012 – Apr 2013 Research Assistant BYU Finance Dept Jan 2009 – Jun 2009  Sorted and parsed large financial data to empirically validate model of options’ high-order return distribution Teaching Assistant BYU Economics Dept Fall 2008  Led a Macro Econ class of 40 students in coursework sessions; designed and presented exam-prep lectures  Graded students’ intricate macro analysis with consistency; followed with recommendations to professor Teaching Assistant BYU Economics Dept Fall 2007  Designed and presented exam review lectures; critiqued 60 weekly essays for quality of analysis  Led weekly coursework sessions, and lent clear exposition in the lab for dept-wide Econ Principles students TECHNICAL SKILLS Yearlong private study of Computer Science, algorithms, C, Python, Linux, parallel processing, machine learning  MOOCs on myriad data science topics; including: Andrew Ng, Stanford; CS 229 Machine Learning David Mackay, Cambridge; Info Thry, Pattern Recog, NN Tim Roughgarden, Stanford, Coursera; Algorithms I Harvard Extension; CS 109 Data Science Geoff Hinton, Coursera; NN for Machine Learning Edx; Scalable Machine Learning – Apache Spark  Parallel Processing with Nvidia CUDA; GPU kernels implemented in C via the CUDA extensions Knowledge of parallel primitives: map, gather, stencil, transpose, reduce, scan, compact, parallel sort, filter  Machine Learning skills: Python (and iPython notebook), and ML/data-munging libraries (numpy, scikit-learn, pandas, Theano) Common ML Algos: Linear/Logistic Regression, K-means, KNN, SVMs, Decision Trees, PCA, FF Neural Nets Deep Learning frameworks: Caffe and Nvidia DIGITS for training Convolutional Nets for image classification  Successfully trained demo model to classify images of prominent buildings LANGUAGES AND TECHNOLOGIES C; Java; Python; MATLAB; R; SAS; SQL; VBA –and to a lesser extent, functional programming in Haskell, Scala Excel 2010, Access 2010 – Microsoft Office Specialist (MOS) Certification

Related Documents