Quantitative Model Validator Advisor (Open to Remote)
- Türkiye
- Kontrat
- Tam zamanlı
- Contribute to the production of the Model Validation Reports through technical analysis and write-ups.
- Recommend improved methods and techniques to provide innovative, thorough, and practical solutions that support business strategies and initiatives, as well as better ways of conducting or assessing 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.
- Counsel teams on company policies and industry-wide modeling practices.
- Inform the team’s technical direction for validation or testing strategies and assessment of quality and risk of model methodologies, outputs, and processes and applying understanding of relevant business context to interpret model results, monitor performance, and assess risks.
- Communicate technical subject matter clearly and concisely to team leadership and project stakeholders.
- 6 years
- Bachelor degree or equivalent
- An advanced degree (PhD. or Masters) in a quantitative field such as Computer Science, Statistics, Biostatistics, Economics (with an Econometrics emphasis), Applied Finance, or Applied Mathematics.
- Experience in developing models or validating models used in credit risk or market risk.
- Experience with a large financial institution which has Enterprise Risk Management functions that meet the needs of highly regulated financial institutions.
- Fast-learning, building on a strong foundation of knowledge to continuously learn new techniques for building and managing model risks well.
- Communicating technical subject matter clearly and concisely, both verbally and through well-written communications including Initial Assessment Reviews and Validation Reports.
- Programming including coding, debugging, and using relevant programming languages
- Experience in the process of analyzing data to identify trends or relationships to inform conclusions about the data
- Expertise in using statistical methods, including: developing and testing hypotheses, using experimental design, and running linear and logistic regressions
- Advanced skills in using computer languages often used in model development such as Python and R.
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