Quantitative Risk Analyst

Quantitative Risk Analyst Philadelphia, PA Job Details Full-time $64,491.00 - $105,949.50 a year 1 day ago Benefits Paid parental leave Health savings account Paid holidays Disability insurance Health insurance Dental insurance 401(k) Flexible spending account Military leave Paid time off Paid military leave Parental leave Vision insurance Qualifications Statistics Portfolio monitoring Pricing Economics Statistics Financial model development Bachelor's degree in statistics Credit risk assessment SAS Software coding Bachelor's degree in finance Spark Regulatory compliance Business analysis Data reporting Mathematics Bachelor's degree in economics Mid-level SAS language Finance Algorithms Analysis skills Spark implementation Bachelor's degree Model deployment Decision making Statistical modeling Industry knowledge of financial regulations Forecasting Lending Data collection Data visualization Bachelor's degree in mathematics 2 years Data visualization projects Communication skills Banking Python Credit analysis Statistical analysis tools Full Job Description Job Description NewLane Finance is seeking an individual to assist the credit and risk modeling and analytics function using data to advance credit risk behavior and quantification of these risk and return tradeoffs through the deployment of models and algorithms to optimize such strategies. This role will be responsible for providing analytical/quantitative input to help develop, implement, and monitor the build of complex commercial small business Expected Default (ED) and Probability of Default (PD) credit default models. The successful candidate will use their business analysis, process, and quantitative knowledge to ensure business intent is matched with modeling outcome, and document development decisions under SR11‐7 guidelines. In addition to responsibilities on individual modeling projects this role will be expected to work on ad‐hoc projects as needed. Communicating model mechanics and articulating nuances to leadership will be an important aspect of the role. This is a great opportunity for someone who is a modeler/statistician/data analyst/coder (or a combination) with experience in commercial small business credit analysis.

Key Responsibilities:

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

Experience:

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

Desired Characteristics:

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

Salary Range:

$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 https://www.wsfsbank.com/about/careers/ 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 . 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.