Data Scientist Ii

General Summary

Elevate is a technology firm which develops next-generation financial products focused on managing life’s everyday expenses.  The Data Science team conceptualizes, develops, deploys, and maintains predictive models using advanced statistical and machine learning methods. These models are used in Elevate’s Underwriting, Account Management, and Operations applications.  Additional responsibilities include developing and implementation of complex analysis to drive business decisions for our organization. The Data Scientist II will have a higher focus on technical ability and out of the box thinking.

Principal Duties and Responsibilities

• Design, Develop and Deploy advanced machine learning models for use in Underwriting, Customer Management, Marketing, and Operations
• Assess, clean, merge, and analyze large datasets adhering to standardized data manipulation techniques and methodology by leveraging Python, R and/or Snowflakes in Elevate’s Cloud Environment
• Proficiency in multiple linear, nonlinear, and other ML algorithms for testing, development and deployment into our underwriting engine in the application of risk management in all of Elevate’s acquisition channels
• Efficiently apply data mining methodologies to minimize credit/fraud losses, maximize response and approval rates, and develop methods to enhance profitability of Elevate products
• Assist in the implementation of scoring models on multiple decision platforms Including cloud
• Provide knowledge and insight on the third party data providers such as Transunion, Clarity/Experian and Equifax to include knowledge of products and data available, effective use of variables, data dictionaries as well as advantages and limitations;
• Maintain clear, detailed model documentation on our Wiki Server by leveraging reproducible research technologies such as Jupyter Notebook, Rmarkdown, etc.
• Interact with business partners to support the needs and goals of all Elevate portfolios, Rock teams, and Pods.

Experience and Education

• Minimum M.S./M.A. in a highly quantitative field (Computer Science, Statistics, Economics, Mathematics, Business or other quantitatively oriented degree) required. Doctoral Degree is a plus.
• At least two years of experience in Data Science, Risk or Modeling for consumer lending; Professional experience waived with Ph.D. Degree in highly quantitative field
• Demonstrated proficiency with advanced statistical modeling and substantial experience with machine learning techniques (e.g., Random Forest, Gradient Boosting, LASSO, Elastic Net, etc.).
• Proficiency with Python is required
• Proficiency with extracting and manipulating data using multiple database technologies such as Snowflakes.
• Good communication skills for communication with Risk Management peers
• Experience in financial services and/or Credit Risk Management or target marketing preferred
• Knowledge of contemporary supervised and unsupervised data mining techniques a plus

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