Finance Risk Analyst

Finance Risk Analyst •This Role is a hybrid role ( 4 Days in office) and will require candidates to be in office day one•

Interview Process:

Please note that interviews for this role maybe be conducted in person, and applicants must be able to attend an in-person interview to be considered.

Job Description:

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.

Performance expectations:

1. Perform in-depth data quantative analysis (e.g., machine learning, advanced analytics, text mining, pattern recognition) from multiple data sources using a variety of tools; analyze various data systems and data sets to Client new insights; apply findings to develop new risk-based collection and recovery strategies 2. Data extraction, cleaning and validation for ad hoc analyses, reporting and model development 3. Provide data-centric consulting to internal customers in order to guide stratagic business decisions 4. Test, implement and document internally developed machine learning models, while adhering to Client policies, procedures, and processes 5. Internally developing, testing, implementing and documenting new collection and/or recovery scoring models, generate quarterly monitoring reports 6. Work with external vendors to test, implement and document new collection and/or recovery scoring models and vendor products, generate quarterly monitoring reports 7. Evaluating and developing expertise in emerging analytics techniques, tools, and methodologies

Required Skills:

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.