AVP - Customer Value Management - BFSI

Job Description:

The position will be directly responsible for realizing the profitable potential of customer value within the business requirements through providing CVM analytic services to CO. businesses and to the Joint Venture partner, as appropriate. The CVM Team will provide state-of-the-art data analytics including propensity modeling, clustering, survival modeling, optimization, price elasticity modeling, strategic analysis, ad hoc analysis, data manipulation and calculating each customer lifetime value opportunity.

Key Responsibilities

- Work with CVM Global Team and Market CVM teams to develop and implement CVM decision tools and solutions, including response models, net conversion models, customer level profitability models and clusters and survival models

- Assist in statistical procedure development which aims at identifying key drivers of growth and profitability of a global insurance portfolio

- Help to convert statistical conclusions into business solutions, and then collaborate with Market CVM teams for solution implementations in a cross-functional and decentralized global business environment.

- Develop new modeling methodologies and perform regular diagnostics to assess model performance

- Work closely with Business Development team in establishing CVM as a distinct CO advantage with partners

Personal Competencies Required:

- Excellent communication skills, Intelligence and action-oriented judgment

- Eagerness to accept increased responsibility and self-motivated

- Understanding of business and role of data in decision making

- Skill in dealing with people

Technical Competencies Required:

- A high level of skill with data extraction, manipulation, and warehousing tools (Oracle, SAS, SQL)

- Knowledge of statistical packages and/or optimization tools (SAS, SPSS, Minitab, R, S+ and/or I-Log)

- Excellent analytic capability with advanced statistics application knowledge (multivariate and discriminant analysis, CHAID, genetic algorithms, multiple and logistic regression) along with a familiarity with optimization techniques including linear and nonlinear programming.

- Knowledge in developing quantitative statistical models in a consumer and B-to-B environment

Education Requirements:

- Degree in Statistics, Operations Research, Econometrics, Engineering, Applied Mathematics, Business Management,

- Knowledge in the fields of Insurance, Consumer Finance, Financial Services, Database Marketing and/or Data-mining.