Model Risk Data Scientist

Bachelor’s, Master’s, or PhD in Data Science, Statistics, Computer Science, or a related field 3 years of work experience in a related technical field, or 5-7 years relevant applied academic experience Proven experience in data analysis, modeling, and performance evaluation Strong proficiency in Python, and specifically data analysis libraries (Pandas, Numpy), Data Visualization (Python matplotlib Plots, Excel Plots / BI tools), and SQL Ability to interpret and communicate complex data insights to both technical and business audiences Exceptional problem-solving and analytical skills with a focus on actionable results Interest in developing deep domain expertise for model risk analysis and model governance work Ability to thrive in a fast paced environment characterized by the need to solve extremely varied, high impact, open ended problems Proven experience in assessing the quality, stability, performance and behavior of production grade ML models, ideally from the perspective of model governance, fair lending or economic risk is highly preferred Familiarity with fraud detection preferred Experience with GitHub

Build foundational processes for generating and surfacing model performance metrics. Craft and calculate metrics and build Python rails. Act as point of contact for Strategic Financial Partners and manage their model governance needs. Conduct performance and exploratory analyses to establish model outcome quality. Provide guidance on product use, respond to inquiries on model development procedures, and generate stability analyses. Create automation to detect inconsistencies or issues with production models. Primarily work with a Python ecosystem, using SQL, SageMaker, S3, Metabase and Git.