Versatile data scientist and engineer. I enjoy research as well as software development; my favorite areas of work are in computational science, statistical computing, modeling, and mathematical optimization. My goal is to develop entire frameworks to improve data science. My work: 1) Research and derive new mathematical models that will quantify business intuition 2) Develop scalable and fast algorithms for said models 3) Architect and collaborate with engineers to build large scale data science systems 4) Make data science accessible through interactive visualization and self serve portals As a side interest I like to study cooperative AI, specifically multiplayer pursuit evasion games and their application to crime as well as search and rescue

Senior Data Scientist @ Data Scientist on the Marketing Science and Algorithms team, a research team at Netflix that focuses on building predictive algorithms, models, and experiments. I am responsible for two things: modeling and engineering large data science systems. My modeling research is in time series, probability models, and optimal decisions. These models serve business applications in growth modeling, customer lifetime value, media mix optimization, campaign optimization, sentiment analysis, financial operations. Strong experience with developing all parts of the data pipeline: ETL, modeling, data visualization, and productionisation. From December 2012 to Present (3 years 1 month) Research Assistant @ Mathematical research in energy consumption. Data mining load data in California. Predicting 1 hour ahead and 1 day ahead loads. Implementation of sparse Vector Auto Regression in R package "fastVAR". Various implementations of matrix imputation in R package "imputation" From September 2011 to December 2012 (1 year 4 months) Math Intern @ Mathematical research in climatology. Developed an estimator for vector autoregression using distributed gradient descent on hadoop. From July 2011 to September 2011 (3 months)

MS, Statistics @ Stanford University From 2011 to 2012 BS, Math and Computational Sciences @ Stanford University From 2007 to 2011 Jeffrey Wong is skilled in: R, Algorithms, Data Mining, Big Data, Machine Learning, Hadoop, Time Series Analysis, Analytics, Statistics, NumPy, SciPy, Data Science, Hive, Apache Pig, SQL