Vodafone - Associate/Deputy Manager - Predictive Analytics

JD for CVM Analytics Team, Vodafone, Pune:

Must Have :

- Knowledge of statistics, predictive modeling, data manipulation

- Must have proficiency in one or more of the following tools: SAS, SPSS, R

- Should be able to handle data and prepare insightful presentations

- Must have hands on experience in one or more of following tools: Excel, SQL, Teradata, Orcle

- Should have 3-8 Years of experience in analytics industry

- Must have done at least one end to end predictive modeling project

- Should have good communication skills

- Should have used one or more of following: Churn, Cross-Sell, Up-Sell, Market Basket Analysis, Optimization, TSP, Forecasting, Segmentation, Up-Lift measurement, Customer acquisition, Fraud detection,, market mix modeling, Collaborative filtering, LTV etc.

- Knowledge of Statistics : ANOVA, Probability distributions, hypothesis testing, t-test, F-test, chi-square test, Information Values, factor analysis etc.

Good to Have :

- Basic knowledge of Telecom industry

- Working knowledge of algorithms: Decision Trees: CART, CHAID, C5.0, Logistic Regression, KNN, ANN, SVM, Linear Regression, Bayesian Networks, PCA, Feature Selection, SNA, Association Rules, K-Means, ARIMA, Random Forest, Bootstrapping and Bagging, etc

Leadership skills, Managerial Skills :

- Manage, oversee and deliver analytics projects which may involve model development, validation, governance, implementation, and end-to-end delivery of solutions

- Conduct analytics training for stakeholders as per business needs

- Develop and enhance relationships with internal and external partners to ensure effective execution of the strategies

- Help prepare and deliver business cases to justify the introduction of new (or updates to existing) strategies

- Recommend suggested solutions to stakeholders which will address issues and opportunities identified through analysis

- The development of strategies to detect and prevent undesirable trends, across the customer lifecycle e.g. predictive models, high risk rules etc.

- The identification of new insight to improve and enhance the existing systems/strategies already in place