The Senior Quantitative Finance Analyst will be a key leader in the Model Risk Management division of Global Consumer, Small Business Banking, and Wealth and Investment Management. The role is especially designed to provide both thought leadership and hands-on expertise in methodology, techniques, and processes in data science and machine learning including, but not limited to, Gradient Boosting Tress, Random Forests, Support Vector Machines, Prototype Methods and Nearest-Neighbors, and Artificial Neural Network in the consumer banking and financing space. The scope of the models, methods, and processes spans across credit, underwriting, marketing, valuation, collections, loss and net income, and optimization in credit cards, first mortgage, home equity, auto loans, small business banking and lending, insurance, and wealth management.
The position will be responsible for
- Performing all model validation tasks including but not limited to independent model validation, annual model review, ongoing monitoring report review, required action item review, and peer review.
- Conducting all administrative and governance activities such as model identification, model approval, breach actions, extension assessments, and system of records, to manage model risk.
- Providing hands-on leadership for projects pertaining to machine learning approaches; and providing methodological, analytical, and technical support to effectively challenge and influence the strategic direction and tactical approaches of these projects.
- Communicating and working directly with relevant modeling teams and their corresponding Front Line Units; and if needed, communicating and interacting with the third line of defense (e.g. internal audit) as well as external regulators and governance agents.
The successful candidate should be a seasoned modeler or validator and meet the following requirements:
- Conducted complete and rigorous independent development and/or validation of models that use machine learning methodologies.
- At least 5-years of work experience at another financial services firm in quantitative research, model development, and/or model validation.
- Graduate degree in mathematics, statistics, computer science, and/or engineering, with a solid knowledge of the banking and finance industry; or possess a graduate degree in finance and/or economics with strong quantitative skills.
- Proficiency in ML platforms/software (e.g., SPM®, Python / sklearn, XGBoost, and R), algorithms, and techniques; and proficient in at least two of the following languages and statistical packages: SAS, SQL, MATLAB, R, VBA, and Python.
- Strong knowledge of financial, mathematical and statistical theories and practices, and a deep understanding of the modeling process, model performance measures, and model risk.
- Strong written and verbal communication skills.
- Knowledge of risk, underwriting, marketing, valuation, optimization and P&L modeling for consumer banking and lending.
- Coaching experience in a modeling group.
- Ability to manage multiple projects and direct the effort of others.
- Business and operations knowledge and/or experience for auto loans, home loans, credit cards and other products in consumer banking, finance and investments.