Manager - Data Modelling (Hadoop)

Corporate Access
  • Bangalore
  • Confidential
  • 6-9 years
  • Views
  • 26 Apr 2017

  • Business Analytics

  • IT/ Technology - Software/ Services
Job Description

Job Description:

1. Passion and deep technical competency in quantitative methods and/or business analytics

2. Proven problem solving skills in industrial settings is a must

3. Proven ability in model building and application experience in data mining techniques and tools (SAS and/or other modeling packages like R, Matlab, Mathematica, ILOG etc)

4. Competent programming aptitude with excellent computation and data mining skills (with expertise in using SAS and/or SQL)

5. Understanding of insurance domain is preferred

6. Ability to translate and articulate technical thoughts and ideas to a larger audience including influencing skills with peers and senior management

7. Self motivated to continuously upgrade one s domain knowledge, keep abreast of latest developments in the field and evaluate its application in the business area on a consistent basis.

8. Exceptional commitment to stay focused in one s pursuit towards excellence with demonstrated results of the same
.
9. All new hires are subject to work eligibility verification

Job Requirements:

1. Graduate degree from a renowned institution in any advanced quantitative modeling oriented discipline including but not limited to Statistics, Marketing Science, Operations Research, Econometrics, Stochastic Finance, Machine learning, Distributed and parallel computing, Digital media analytics, etc.

2. 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/or publications in reputed journals or conferences.

3. 6-9 years of work 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.

4. Working knowledge 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.

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

a) 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.

b) 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.

c) 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.

d) Advanced statistical methods including complex multivariate statistical methods, discrete choice modeling, conjoint based analysis

e) Machine learning including Bayesian methods, reinforcement learning, Neural networks, Support vector machines, Hidden Markov Models, relevance vector machines, Probabilistic/ Evidential Reasoning

f) Operations Research (Queuing, Markov Models, DEA, Integer Programming, Dynamic programming, Stochastic Programming, Game theory)

g) 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.


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