Quantitative Risk Analyst

• Contract
• Hybrid - In office requirement of 4 days per week

• Technical finance or analytical/statistical background and awareness of risk management and automotive financial services concepts.
• Experience in database design with expert knowledge of SQL (SAS or similar programming language preferred) and MS Office.
• Experience in the use of Python and machine-learning techniques applied to risk modeling is a plus.
• Strong written and verbal communication skills necessary to interact with multiple stakeholders at all levels of the organization.
• Experience participating in cross departmental projects, and credit decision engine is highly desirable.

• Develop, implement and monitor statistical analytical tools and systems, with a focus on the acquisitions and collections process.
• This data-centric role aims to analyze and solve problems by employing expertise from various disciplines, such as statistics, mathematics, and machine learning in support of well-defined business goals.
• Participate in global Risk Management projects to ensure that best practices are utilized as it pertains to scoring and pricing quantification. Position may require ICS compliance activities as applicable.

• Perform in-depth data quantative analysis (e.g., machine learning, advanced analytics, text mining, and pattern recognition) from multiple data sources using a variety of tools; analyze various data systems and data sets to discover new insights; apply findings to develop new risk-based collection and recovery strategies.
• Data extraction, cleaning and validation for ad hoc analyses, reporting and model development.
• Provide data-centric consulting to internal customers in order to guide strategic business decisions.
• Test, implement and document internally developed machine learning models, while adhering to DFS policies, procedures, and processes.
• Internally developing, testing, implementing and documenting new collection and/or recovery scoring models, generate quarterly monitoring reports.
• Work with external vendors to test, implement and document new collection and/or recovery scoring models and vendor products, generate quarterly monitoring reports.
• Evaluating and developing expertise in emerging analytics techniques, tools, and methodologies.

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