David Purdy's Email Addresses & Phone Numbers

Quantitative Researcher @ Teza Technologies 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)...

Quantitative Researcher @ Teza Technologies 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) Chicago, ILSummer Associate @ Morgan Stanley Graduate summer associate in Process Driven Trading (PDT) group, and developed strategies for systematic trading. From 2008 to 2008 (less than a year) Associate Researcher @ FireSpout 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 @ National Security Agency 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) Advisor to multiple startups and founders @ Advisor I advise companies and founders on strategy, especially addressing data science platforms, teams, and products. These companies range from major multinationals to stealth-mode startups, addressing consumer and enterprise markets. If you're interested in higher performing teams, accelerating time to market and DS research loops, and developing world class DS platforms, let's talk. Chief Scientist @ Ghost Led Data Science strategy and execution for machine learning, system evaluation, data quality, and safety evaluations. Ghost is an autonomous vehicle startup in Mountain View, CA. From April 2019 to December 2019 (9 months) Mountain View, CAVice President @ Goldman Sachs I had two roles as a Strat (Quant): High frequency trading of interest rates products (Treasuries, Futures, etc.), and as a Divisional Strat. The Div Strats team is responsible for analyses and modeling of global activity - sales & trading - across all of the asset classes in Goldman's trading division.In HFT, I led the development of strategies that achieved more than 5X increase in profitability of the team's market making strategies, despite historically low benchmark interest rates. My team generated hundreds of improved models, with more than 30 released model bundles in about 30 weeks. We achieved >10X greater productivity using a modeling & deployment framework I developed, which allowed for very fast research iterations and rapid productionization.As a Div Strat, I led the Securities Division's efforts to aggregate, analyze, and leverage its overall trading volumes and market share data. I was responsible to the COO and CFO of the Securities Division. Our aims included: optimized marketing & selling, performance benchmarking, and other competitive and commercial analyses. I led an effort spanning 8 asset classes, comprising 70 engineers, strats, and stakeholders.Notable achievements:- Developed many signals and strategies, responsible for significant profit for the IRP HFT trading desk (worked in C++, Slang, and R)- Led Strat due diligence on many FinTech investments and vendors, including two sizable investments- Designed framework & syntax for industrial machine learning and rapid deployment- Novel visualization methods for high resolution visualization of HFT order book & market activity- Novel visualization and algorithmic guidance for massive tabular time series data (for divisional CFO & COO reporting)- Started firmwide Data Science community, grew to ~300 people globally; had over 800 people by 2016- Conceived and defined a firm-wide data catalog, with leadership and development responsibilities shared across all major divisions From July 2012 to October 2014 (2 years 4 months) Greater New York City AreaSr. Statistician and Machine Learning Engineer @ Theranos 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) Palo Alto, CATeam Partner @ The Ensemble The Ensemble tied for the highest score in the Netflix Prize Competition, a $1 million competition to develop the best recommendation system in the world. From 2009 to 2009 (less than a year) PhD Candidate @ UC Berkeley From 2004 to 2009 (5 years) Researcher @ Altavista Responsibilities and contributions included: development of summarization algorithms, global ranking quality metrics, improvements to freshness algorithms and metrics, web spam identification heuristics.Altavista was acquired by Overture, and Overture was acquired by Yahoo. From May 2002 to November 2003 (1 year 7 months) Sr. Data Science Manager for Safety Data Science @ Uber At Uber, I had the good fortune to lead and launch foundational efforts. In my final two years, I led the Safety Data Science team, which took on some of the hardest and highest priority challenges of the company by making rare events rarer.In addition, I launched and architected our teams and platforms for machine learning (Michelangelo) and marketplace forecasting, for demand & supply forecasting in high resolution of time and space (FutureFlow). These have been the foundation upon which scores of new applications have been developed, adding hundreds of millions of dollars of incremental annual revenue.Overall, I tackled strategic & tactical problems, both technical and organizational, that have included:* Architected Uber's platform for Machine Learning* Architected Uber's system for high resolution spatiotemporal forecasting* Started the ML Platform team and ML Data Science team* Manager of numerous data scientists developing machine learning and statistical models* Launched large new efforts and new teams, setting their vision and roadmaps* Recruited large numbers of statisticians and other quants* Recruited managers in: Product, Data Science, and Engineering. * Introduced substantial changes to DS recruiting processes, including interviews, outreach, campus & conference recruiting, and offers* Multiple patents awarded* Due diligence on external organizationsUber's rapid growth has given me the opportunity/responsibility to fill in for other roles at times: product manager, engineering manager, and recruiter. From October 2014 to April 2019 (4 years 7 months) San Francisco Bay Area