Quantitative Risk Analyst
- Assist the Quantitative Risk Manager in constructing a Credit Decision Scorecards and statistically based credit risk modeling strategies based on quantitative modeling methods (e.g., good / bad definition, performance sample windows, sample size and exclusions).
- Assist in developing and implementing a framework for data collection, processing and analyzing customer and 3rd party data (e.g., PayNet, D&B, consumer credit bureaus) for implementing credit risk strategies
- Plan and execute self-driven analytics on large data sets (structured and unstructured data) using next generation technologies, prepare analysis and reports to support discussions on key analytics and model aspects to drive decision making
- Validate credit default rates from portfolio attributes (e.g., delinquencies, EOD, loss curves, dealer performance) and make recommendations on credit model and policies
- Work with sales management on risk-based pricing strategies optimizing dealer conversion rates and profitability.
- Oversight of credit data mart used for reporting and portfolio performance monitoring.
- Supporting ongoing and future projects working with the senior team.
- Ability to create visualizations of data and/or quantitative information for management decision-making
- Support building and enhancing procedures and model documentation in compliance with regulatory guidance as well as the Bank's model risk policy
- Maintain current/develop new analytical reports and presentations for senior management, executive committees, and regulatory exams
- Bachelor's degree in Mathematics/Statistics, Operations Research, Economics, Finance, or other quantitative discipline; or in lieu of a degree, four (4) plus years' experience in Risk, Finance, Consumer Lending
- Three (3) plus years of commercial small business credit modeling experience.
- Two (2) plus years of experience in Consumer Lending statistical modeling/analytics, preferably related to ALL and/or Loss Forecasting modeling for credit cards.
- Two (2) plus years in coding with Python, PySpark or other equivalent language within the past Five (5) years
- Demonstrated experience with SAS and other statistical methods.
- Proven decision-making role constructing credit models in a regulated environment
- Strong quantitative and analytical skills in statistical analysis and data science best practices
- Strong communication and partnering skills
$64,491.00 - $105,949.50 Individual base pay may vary on additional factors such as the candidate's experience, job-related skills, relevant education, geographic location, and other specific business and organizational needs. In addition to base salary, WSFS Financial Corporation (WSFS) and its subsidiaries may offer eligible Associates discretionary and formula-based incentive and retention awards. WSFS provides a competitive benefits package, which includes medical, dental, and vision coverage; a 401(k) plan; life, accident, and disability insurance; flexible spending accounts (FSAs) and health savings accounts (HSAs); and wellness programs. Additional benefits may include paid parental leave, military leave, vacation and other paid time off, sick leave in accordance with applicable state laws, and paid holidays. Benefit offerings are subject to eligibility requirements, legal limitations, and may vary based on an Associate's location and employment status. For more information about Associate benefits, please visit WSFS Bank is inclusive and supportive of individual needs. If you have a physical or other impairment that might require an accommodation, including technical assistance with the WSFS Bank Careers website or submission process, please contact us via email at [email protected]. WSFS is an equal opportunity employer. We do not discriminate based upon race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.
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