Data Analyst - Statistical Modeling (6-12 yrs) Bangalore/Chennai/Hyderabad/Mumbai (Analytics & Data Science)
Randstad India
1d ago
source :

Roles & Responsibilities :

  • Develop statistical methodologies and deploy analytical tools to support different business initiatives
  • Continual enhancement of statistical techniques and their applications in solving business objectives
  • Compile and analyze the results from modeling output and translate into actionable insights
  • Acquire and share a deep knowledge of data utilized by the team and its business partners
  • Participate in global conference calls and meetings as needed and manage multiple customer interfaces
  • Execute analytics special studies and ad hoc analyses
  • Evaluate new tools and technologies to improve analytical processes
  • Set own priorities and timelines to accomplish projects (accountability for project deliverables)
  • Further development of relevant analytical tools and processes in close cooperation with the global teams, especially with regard to European aspects
  • Definition of new tasks and the necessary requirements for them according to specifications
  • Identification and evaluation of new data sources, evaluation of suppliers and estimation of the potential use of the data
  • Presenting results including preparing recommendations for management decisions
  • Mandatory knowledge and experience in linear regression model, distribution in logistic regression model, Classification (decision) tree.
  • Build predictive models including but not limited to credit risk. fraud, response, and offer acceptance propensity
  • Identify new data sources / patterns that add significant lift to predictive modeling capabilities
  • Conduct analysis and turn insights into actionable changes for predictive models or policies
  • Efforts will focus on the following key areas :

  • Various classical Statistical techniques such as Regression, Multivariate Analysis, etc.
  • Data Mining & Text Mining
  • Time Series based forecasting modeling
  • Customer Segmentation Cluster & Factor Analysis
  • Customer Relationship Management Analytics
  • Demand Modeling
  • Statistical Scoring models to be used in Risk / Marketing domains
  • Market Mix Modeling
  • Structural equation modeling for identifying accelerators of customer satisfaction and loyalty
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