Manages and builds systems of models to analyze diverse big data sources
Manages and participates in developing, testing and validating models that drive business value
Assists with identifying and interpreting insights from data
Direct leadership of assigned data science team
Manage and participate in working with large data sets to solve unstructured problems using different analytical and statistical approaches
Manage the sourcing, ingesting, and cleaning of data sets in preparation for analysis
Ensure data is stable, accounting for complex data drift in development and production
Manage the building of econometric, statistical and machine learning models for various problems
Manage committing of complex coding into model repository
Lead the selection and refinement of models taking into account performance, reliability and stability metrics
Develop model refinement educational materials and deliver related training for data users
Create outputs from multiple models for business discussions
Lead stakeholder meetings to discuss concerns, opportunities and production challenges
Review own and assigned team’s code to ensure it is efficient, accurate, and using best practices
Understand and adhere to the Company’s risk and regulatory standards, policies and controls
Requirements
Bachelor’s degree and a minimum of 7 years related experience, or in lieu of a degree, a combined minimum of 11 years higher education and/or work experience, including a minimum of 7 years related experience
Minimum of 2 years managerial, supervisory and/or work leadership experience
Intermediate experience working with multiple statistics and following data science principles such as AB testing, sample selection, hypothesis testing, and modeling bias
Intermediate proficiency with pertinent statistical software and languages and tools
Experience with various hybrid databases both on premise and in the cloud
Intermediate level knowledge of Structured Query Language (SQL) and Not Only Structured Query Language (nSQL)
Expert understanding of modeling techniques such as Bayesian modeling, Classification models, Cluster analysis, Neural Network, Non-parametric methods, and Multivariate statistics