AVP / VP, Data Scientist

Full Time

International Banking Client

Office Status: Remote


  • 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.
  • Develop and deploy programs and packages in Python that can be used to efficiently process data, generate reports, automate policy checks.
  • Perform new product risk analysis, stress testing, sensitivity testing Loss forecasting, etc.
  • 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.
  • 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.
  • Capture business requirements and reflect them in analysis, develop and present recommendations in business contexts, such as their association with risk drivers, the causation to losses, etc.
  • Collaborate with cross-functional business partners on new initiatives, project requests, prioritization, and delivery.
  • Perform model/non-model/EUC onboarding and design monitoring controls for them.
  • Develop model documentation and work with compliance and governance teams to effect industry regulatory requirements.
  • Develop new interaction attributes and test them in the models.
  • Develop Business Intelligence reporting framework.
  • Support Credit strategy teams using analytical methods such as segmentation, hypothesis testing, swap-set analysis, etc.
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