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Data management teams need strong cloud computing and database management skills, and proficiency with tools like Hadoop, mapreduce jobs and SQL queries. The analysts need to be deep thinkers and creative modelers with experience in machine learning and financial modeling—ideally both. Being model- and data-driven means overnight data-crunching to produce daily data reports, which often lead to more overnight questions. There are also inherent difficulties in talking to clients and internal stakeholders when inherently unstable data and statistics are key tools for decision-making.
From a process standpoint, we need start asking new kinds of questions that Big Data is opening up for the first time. Speaker Cathy O’Neill will use her unique experience in finance, which is the field that is the most developed in terms of modeling, to explain how she sees today’s business world as relatively unsophisticated and ""spoiled for data."" She’ll explain various techniques that financial analysts employ to improve models, and reconsider the practice of A/B testing in a model-driven world.
Cathy O’Neil earned a Ph.D. in math from Harvard, was postdoc at MIT in the math department, and a professor at Barnard College where she published a number of research papers in arithmetic algebraic geometry. She then chucked it and switched over to the private sector. She worked as a quant for the hedge fund D.E. Shaw in the middle of the credit crisis, and then for RiskMetrics, a risk software company that assesses risk for the holdings of hedge funds and banks. Since this spring she’s been a data scientist for the startup media company “Intent Media”;http://www.intentmedia.com/.