Uber is hiring über data scientists! We prize impact and innovation. At Uber you will do work that will really move people. We're open to many backgrounds, and I am especially interested in people with backgrounds in data science + one or more application areas of fraud, logistics & transportation, anomaly detection, and market segmentation. Our interests are wide, spanning areas as diverse as spatial statistics, ethology, and behavioral economics. In my team, expertise in statistics and machine learning are very important. If you want to go deep in data science and have an impact, let me know! Here is the posting for opportunities with my team, Algorithmic Insights: https://www.uber.com/jobs/44336 ___________________ My experience involves R&D in government and industry, relating to quantitative finance, personalized medicine, natural language processing, information retrieval, and other applications of statistics, especially machine learning. My research has focused on machine learning for very large, very high dimensional data sets; interactive visualization of machine learning models and exploratory model analysis. Specialties: Statistics and computing: Statistics, Machine Learning, Data Visualization, High Performance Computing, Recommendation Systems, Collaborative Filtering. Quantitative Finance: High Frequency Trading, Statistical Arbitrage. Medicine: Biomarkers, Applications in Personalized Medicine. NLP and Languages: Natural Language Processing (NLP), Information Retrieval (IR), Statistical Machine Translation, Chinese and Japanese.

Data Science Manager @ From October 2014 to Present (1 year 3 months) San Francisco Bay AreaVice President @ From July 2012 to October 2014 (2 years 4 months) Greater New York City AreaQuantitative Researcher @ Developed the first profitable strategy at Teza. Quant researcher focused on high frequency trading strategies across multiple asset classes. Developed research toolkit that allowed for very rapid iteration cycles allowing quick testing of new ideas and an efficient pipeline for deploying models to production. From July 2010 to May 2011 (11 months) Sr. Statistician and Machine Learning Engineer @ Led statistical and machine learning R&D in partnership with our chief medical officer, chief scientist, and leading medical research centers around the world. Designed machine learning and statistical research platform, data warehousing and complex event processing framework to deploy machine learning models. From 2009 to 2010 (1 year) Team Partner @ The Ensemble tied for the highest score in the Netflix Competition, a $1 million competition to develop the best recommendation system in the world. From 2009 to 2009 (less than a year) PhD Candidate @ From 2004 to 2009 (5 years) Summer Associate @ Graduate summer associate in Process Driven Trading (PDT) group, and developed strategies for systematic trading. From 2008 to 2008 (less than a year) Researcher @ From March 2003 to October 2003 (8 months) Researcher @ From 2002 to 2003 (1 year) Associate Researcher @ I developed new algorithms for document summarization, topic identification, and other areas of natural language processing and information retrieval. Also involved in technical presentations to secure angel and VC funding, recruiting, and mentorship. From June 2000 to November 2001 (1 year 6 months) Greater Boston AreaCryptanalytic Mathematician @ Received full-tuition NSA scholarship and year-round stipend. Worked at the Agency during summers and after graduation. My interests in computational number theory, high performance computing and foreign languages flourished as a result. Also selected for the Director's Summer Program (DSP). From August 1995 to June 2000 (4 years 11 months)

PhD, Statistics @ University of California, Berkeley From 2004 to 2009 AB, Mathematics @ Harvard From 1996 to 1999 Mathematics @ California Institute of Technology From 1995 to 1996 High School @ Oklahoma School of Science and Mathematics From 1993 to 1995 David Purdy is skilled in: Machine Learning, Statistics, R, Natural Language Processing, Data Visualization, Quantitative Finance, Statistical Computing, Statistical Modeling, Statistical Learning, Time Series Analysis, Information Retrieval, Monte Carlo Simulation, Big Data, Statistical Arbitrage, Recommender Systems