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Leadership Hiring at Saaki Argus & Averil
Views:9722 Applications:162 Rec. Actions:Recruiter Actions:29
Data Scientist - BFSI (2-5 yrs)
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
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