utions that support business strategies and initiatives, and conduct ad hoc quantitative analyses, modeling, or programming using SAS, SQL, R, or Python.
- Utilize advanced data mining and/or statistical techniques to develop analytic insights, sound hypotheses, and informed recommendations. Identify opportunities to apply quantitative methods to improve business performance and/or resolve complex or unusual business issues.
- Integrate established company policies and industry-wide modeling practices into department processes. Ensure compliance across modeling projects.
- Design and execute effective validation or testing of models and assess the quality and risk of model methodologies, inputs, outputs, and processes. Apply understanding of relevant business context to interpret model results, monitor performance, and assess risks.
- Communicate technical subject matter to senior management and project stakeholders clearly and concisely, both verbally and through written communication, such as whitepapers or review reports.
Qualifications
THE EXPERIENCE YOU BRING TO THE TEAM
Minimum Required Experiences
- 6 years of relevant work experience, including developing or validating quantitative models.
- Bachelor degree or equivalent in a quantitative field such as Economics, Finance, Statistics, Mathematics, Computer Science, and Engineering.
Desired Experiences
- PhD in Economics w/ Econometrics focus, Applied Finance, Statistics, Mathematics, Computer Science, Engineering, or similar quantitative discipline.
- Experience with retail loan products, home mortgage products preferred.
- Experience in working with huge datasets.
- Work experience at a large financial institution (SIFI or GSIB).
- Experience with model risk framework and oversight.
- Experience in econometrics, statistical inference and/or financial mathematics, including time series forecasting, stochastic processes, hypothesis testing and causal inference.
- Expertise with machine learning and other modern modeling techniques including the use of big/unstructured data.
- Familiarity with the broader AI/ML landscape, including new development in AI/ML.