Data Scientist - BFSI

Experience : 2+ years & above

Work Location : Bangalore

Key Responsibilities :

- Identify, develop and implement the appropriate statistical techniques, algorithms and data mining analysis to create new, scalable solutions that address business challenges

- Innovate new modelling and machine learning approaches

- Communicate findings to the appropriate teams through insights

- Define and develop, maintain and evolve data models, tools and capabilities for predicting

Provide solutions : Customer Segmentation & Targeting, Propensity Modeling, Churn Modeling, Lifetime Value Estimation, Forecasting, Recommender Systems, Modeling Response to Incentives, Marketing Mix Optimization, Price Optimization

- Create interactive tools using cutting-edge visualization techniques (beyond standard visualization like Tableau, Spotfire, Qlikview etc.)

- Ability to work with various forms of structured, semi-structured and unstructured data sources

- Take responsibility for technical skill-building within the organization (training, process definition, research of new tools and techniques, etc).

Skills required :

- Statistical Modeling, both Data Driven and Model Driven approaches (Factor Analysis, Cluster Analysis, Decision Trees, Conjoint Analysis, Regression, ANOVA, Exponential Smoothing, ARIMA and Structural Equation Modelling)

- Choice-Based Conjoint Analysis - predictive validity of multinomial logit extant models, latent class model, single multivariate normal distribution, mixture of multivariate normal distributions and Dirichlet Process Mixture (DPM) Model

Regression : Linear, Multiple, GLM, Discrete choice, Logistic, Multinomial logit, Mixed logit, Probit, Multinomial probit, Ordered logit, Ordered probit, Poisson Multilevel model, Fixed effects, Random effects, Mixed model, Nonlinear regression, Nonparametric, Semiparametric

Statistical classification : Linear classifiers (Fisher's linear discriminant, Logistic regression, Naive Bayes classifier, Perceptron), Support vector machines (Least squares support vector machines), Quadratic classifiers, Kernel estimation (k-nearest neighbor), Boosting (meta-algorithm), Decision trees (Random forests), Neural networks, Learning vector quantization

- Detailed study of seminal research papers in marketing management and marketing strategy