Advanced Analytics VP Specialized Analytics Mgr 2 GDM Job in Long Island City, New York US
Reference Code: 11046936
Location: Long Island City, NY, USA
Education Level: Doctorate Degree
Description
- PhD/Post Doc from a renown institution in any advanced quantitative modeling oriented discipline including but not limited to Machine learning, Statistics, Marketing Science, Operations Research, Econometrics, Stochastic Finance, Distributed and parallel computing, Digital media analytics, etc.
- Field of post graduation - Computer Science, Mathematics, Operations Research, Statistics, Econometrics, Management Science and related fields. Could be any graduate degree holder.
- Strong academic record and publications in reputed journals or conferences.
- 6-9 years of post-PhD experience in the field of advanced quantitative techniques while working for leading global academic institutes or corporate innovation research labs or analytics organizations of large corporate or in consulting companies in analytics roles.
- Sound knowledge and application in as follows:
- Advanced statistical methods including complex multi-variate statistical methods, discrete choice modeling, conjoint based analysis
- Machine learning including Bayesian methods, reinforcement learning, Neural networks, Support vector machines, Hidden Markov Models, relevance vector machines, Probabilistic/ Evidential Reasoning
- Operations Research (Queuing, Markov Models, DEA, Integer Programming, Dynamic programming, Stochastic Programming, Game theory)
- Macroeconomic modeling, Leading indicator analysis, Long term and near term Forecasting, Time series based methods, Bayesian multivariate regression methods, ARCH/GARCH/VAR models and other advanced regression methods, Mathematical economics, System dynamics, Stochastic control, Nonlinear dynamic models, etc. Prior practical industrial scale modeling exposure is a must.
- Advanced quantitative methods relevant to modeling consumer experience in the digital world. Experience in web log mining for visitor segmentation, visitor behavior modeling, common path analysis, conversion analysis, abandonment analysis, promotion analytics, buzz analysis, sentiment analysis, social networking analysis etc is a must.
- Latent class models, Multivariate logit/probit/tobit models, Multinomial logistic models, Marketing mix modeling, Hidden Markov models, Conjoint methods, Market research and optimization methods. Prior experience in customer mindset modeling, customer loyalty, customer choice, brand equity, advertisements/promotion mix, etc is a must.
- Parallelizing existing traditional or modern (machine learning) based algorithms, Randomized algorithms, Simulations and Simulation based methods including Markov Chain Monte Carlo, parallel and distributed simulations, next gen optimization methods, etc. Knowledge of Hadoop/grid based programming for large scale problem solving is a must.
September 7, 2011
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