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Decision Science Analyst
Location:
Los Angeles, CA
Description:
JOB DUTIES: Responsible for building, managing, and implementing Credit Risk and Loss forecasting models using Python/Pyspark. Have deep statistical and analytical knowledge and be adept at data wrangling, logistic regression, CART/CHAID concepts and application of latest Machine learning techniques like Extreme Gradient Boosting, Random Forest, LightGBM etc. Assist with the Model Governance function and work closely with cross-functional business partners in Credit, Fraud, Finance, Compliance, Data Engineering and Model validation. Specific duties include: 1) Develop Credit risk and fraud models for underwriting, portfolio management, and Collections using logistic regression, ML techniques like Random Forest, Light GBM, and/or Extreme Gradient boosting. 2) Develop and deploy programs and packages in Python that can be used to efficiently process data, generate reports and automate policy checks. 3) Perform new product risk analysis, stress testing, sensitivity testing Loss forecasting, etc. 4) Evaluate new data sources and identify business insights in big datasets using statistical modeling and analysis methodologies, considered methodologies include but are not limited to linear regression, logistic regression, decision trees, linear/non-linear optimization, and multivariate statistical analysis using Python/PySpark. 5) Convert complex data and findings into understandable tables, graphs, and written reports using Python and/or visualization tools such as Looker, Tableau, etc., and communicate strategies/proposals/solutions to key stakeholders. 6) Develop model documentation and work with compliance and governance teams to effect industry regulatory requirements. Telecommuting permitted from any location in the U.S. JOB REQUIREMENTS: Requires a Masters degree in Mathematics, Statistics, Computer Science, Economics or related field, plus two (2) years of experience in decision science, data science, data analysis or related quantitative analysis experience, including experience in Python/PySpark programming; data wrangling; and developing statistical models using Logistic regression and Machine Learning techniques. * Specific skills: Position also requires education or experience in: 1) Data reporting tools such as Tableau or Looker; 2) Machine learning techniques such as XGB, LightGBM and Support Vector Machine; 3) Model governance and regulatory environment; 4) Credit bureau data and alternative data sources; and 5) Storytelling to capture and convey data insights *Any level of knowledge, experience and/or coursework in the specific skills is acceptable. Telecommuting permitted from any location in the U.S. The salary for this position is $119,000 per year. To apply, send resume to: Juan Donis/
Posted:
August 27 on Jobvertise
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