Data Diagnostics Leader - CoE - Insurance

We are currently partnering search for Data Diagnostics Leader with a CoE of a leading Insurance Player

Primary Requirements:

- PhD/Post-Graduate degree from a renowned institution in any advanced quantitative modeling oriented discipline including Computer Science, Mathematics, Operations Research, Statistics, Econometrics, Management Science and related fields

- 10+ years of total work experience & at least 5 years of experience in big data in the field of advanced quantitative techniques while working for leading global academic institutes/ corporate innovation research labs/ analytics organizations of large corporate or consulting companies

- Strong (at least 7+ years) leadership / management experience in an analytic environment in the industry, managing other experienced data scientists and analysts

KRA's:

- Develop a CoE with deep technical competency in quantitative methods and business analytics to solve pressing business problems

- Drive a strong people agenda to manage and inspire data engineers and analysts with technical expertise, domain knowledge, business experience and statistical/modeling techniques

- Generate demand for high quality data analytics projects

- Lead multiple, complex projects simultaneously

- Problem solve business requirements from various product/business/functional owners

- Have a strong understanding of the Insurance domain to be able to solve complex business issues

- Translate and articulate technical thoughts and ideas to a larger audience including influencing skills with peers and senior management

- Exceptional commitment to stay focused in one's pursuit towards excellence

What technical skills are you expected to have?

- Proven, hands-on experience in Big Data applications/solutions such as Mahout, Hive, HBase, Cassandra, Pig, no-SQL, etc. Knowledge of Hadoop/grid based programming for large scale problem solving is a must

- Strong applications skills around SAS and/or other modeling packages like R, Matlab, Mathematica, ILOG

- Sound knowledge and application in at least some of the following fields:

- Advanced quantitative methods relevant to modeling consumer experience in the digital world. Experience in web log mining for visitor segmentation, visitor behavior modeling, buzz analysis, sentiment analysis and social networking analysis

- Latent class 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

- 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.

- 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