Quantitative Finance & Risk Modeling Leader

Fifty-three years have passed since Black, Scholes, and Merton published their seminal work on options pricing. It is astonishing to reflect on the events over the past half century; the collapse of LTCM, the frailty of the RMBS market, and repeated stories on the misuse of financial derivatives. Now, the quantitative finance industry is bracing for a tectonic shift in the form of AI. These examples justify the need for experienced and qualified quantitative professionals in this ever changing and dynamic discipline.

My professional life is dedicated to the field quantitative finance. My passion for this discipline is rooted in the fact that it encompasses and combines the areas of mathematics, finance and computer science. During the course of my career, I have attained expertise in a range of quantitative methodologies and financial products. Based on my experience, I am seeking positions that will naturally utilize these skills to drive profitability for businesses by quantifying opportunities in markets. To facilitate this and to provide a range of support for your business, I hold a Series 7 license. Emotional intelligence, extreme ownership, open communication, transparency, and collaboration are the keys to my success. At my latest position as a Quantitative Developer, I supervised and led the implementation of CDS/CDX products into a newly designed strategic risk system for high grade and high yield trading businesses. This included redesigning pricing models and analytics for deployment and modeling the life cycle of events to accurately capture P&L. My responsibilities extended to manage the onboarding of other fixed income and structured products.

In my previous role, I was responsible for the performance and maintenance of fixed income, option, and swap pricing models used in the risk management of trading desks and portfolios. As part of my responsibilities, I have developed machine learning algorithms for estimating econometric based volatility models for high frequency datasets. Statistical forecasting and general linear/nonlinear model are additional areas of specialization. C++, R, and Python, including the Pandas and Numpy packages, are the primary languages my models and data analytics have been developed in. Throughout my career I have managed and been proactively involved in processes which encompass the entire modeling lifecycle. I have a Ph.D. in mathematics from the City University of New York where my research interests were in the field of stochastic analysis and applied probability theory. I am currently an associate adjunct professor in the Department of Mathematics and Statistics at Hunter College where I teach graduate courses in statistical modeling, machine learning, and programming languages. Given my technical background, industry experience, and committed work ethic, I am confident I can contribute immediately to your team, while continuing to develop into the required role. Thank you for taking the time to consider me as an applicant.

Sincerely,

Subir Dhamoon, Ph.D.

Subir S. Dhamoon, Ph.D. Citizenship: USA

Jersey City, NJ, 07302 email: ******@*****.*** Voice: 917-***-**** LICENSES/ QUALIFICATIONS

FINRA Registrations: Series 7 Programming: C++, Java, Python, R INDUSTRY EXPERIENCE

Wells Fargo, Corporate & Investment Bank, New York, New York August 2023-March 2026 Quantitative Strategies, Front Office Quantitative Developer Supervise and lead the implementation of credit products into newly designed strategic risk system for high grade and high yield trading businesses.

• Redesigned CDS, CDX and fixed income pricing models and risk analytics for deployment in new strategic risk system using Java. Process required extensive development of unit and regression tests in accordance with the principles of SDLC.

• Collaborated with traders and front office teams to model the life cycling events for credit products to ensure accurate capture of P&L for desks in downstream accounting and modeling systems. In conjunction with IT teams, established data pipelines consisting of trade feeds which populated the strategic risk system for fixed income desks.

• Implemented daily monitoring reports for credit products to review the performance of pricing and analytic models across different systems and environments.

Royal Bank of Canada, Capital Markets, New York, New York November 2016-August 2023 Global Risk Analytics, Quantitative Researcher

Lead and manage the development and maintenance of quantitative models for market risk and counterparty credit risk, used within front office risk management systems and for regulatory capital calculations.

• Responsible for the performance and maintenance of fixed income, option, and swap pricing models used in the risk management of trading desks and portfolios. Oversee VaR/Stress VaR risk factor shock methodologies for equity, FX, credit and IR products.

o Tasks include enhancement of model code and methodologies, collaborating with trading desks to resolve operational issues and remediation of model validation/internal audit findings.

• Developed machine learning algorithms for high frequency datasets to forecast VaR/Stress VaR over stress market scenarios using Python. Algorithm employs decision trees to identify and calibrate AR-GARCH models. The volatility models are utilized to simulate stressed P&L vectors for VaR calculations. Additional algorithms have been deployed to identifying data anomalies and outliers which impact model performance.

• Design and program quantitative analytics for large datasets used in front office risk management platforms. Analytics include statistical forecasting, general linear/nonlinear models, and volatility analysis.

• Team lead for the development, implementation, and maintenance of Market Risk Weighted Assets (RWA) projection models utilized for Federal Reserve Bank stress tests (CCAR). Formulated and implemented new quantitative techniques for VaR/Stress VaR forecasting models to resolve modelling deficiencies.

• Supervise the maintenance and model risk management of CCAR Credit Valuation Adjustment (CVA) models for IR caps/floors, swaps, Bermudan swaptions, and FX forwards. o Review and assess methodology differences of CVA and pricing models in various engines through multiple benchmark analysis. Test and review calibration of CVA simulation model parameters.

• In collaboration with senior committees, manage the governance and oversight of market and CVA model development teams across the Enterprise.

• Work with department heads with project planning and resource management in the year round efforts of developing new models, enhancing current models, and supporting new business and strategic initiatives. Federal Reserve Bank of New York, New York, New York January 2012-May 2016 Model Risk Department, Financial Institution Supervision Group, Senior Associate Contributed to a wide range of regulatory mandates through fulfilling a diverse set of leadership roles. Acquired subject matter expertise in the areas of market risk capital models and model risk management.

• Federal Reserve Bank of New York representative for the Trading Book Group (TBG); an international working group housed under the Basel Committee on Banking Supervision (BCBS). o Enhanced the policy development process by initiating industry outreach to U.S. Global Systemically Important Bank’s (G-SIBs) to resolve issues related to the Fundamental Review of the Trading Book

(FRTB). Acquired in-depth understanding of quantitative methodologies employed in the risk management of derivative portfolios.

o Contributed in the design and analysis of FRTB Quantitative Impact Studies (QIS). The results from the analysis were utilized in the calibration of regulatory model parameters. o Collaborated with members of Basel Committee working groups to revise and edit policies related to the capital requirements for banks with exposures to central counterparties (CCP).

• As a member of the Model Risk Management system leadership team, supervised the implementation of SR 11- 07 for financial institutions across the system. Participated or led in multiple model risk management exams and analyzed best practices across firms’ model development, validation, and governance risk control infrastructure.

• Participated in onsite exams at U.S. G-SIBs focused on front office CVA models and risk controls for counter party credit risk.

• Team lead for the construction of models to assess the performance and limitations of risk methodologies at trading desks across U.S. G-SIBs.

o Assessed VaR model calibrations and model assumptions through the analysis of the volatility structure of P&L data via GARCH and econometric models. Developed a suite of statistical algorithms to analyze VaR breaches of trading portfolios.

• Attained subject matter expertise on Basel 2.5 Market Risk Capital Rule. o Oversaw the development of technical infrastructure used to produce analytic surveillance reports which tracked market RWA requirements under the Basel 2.5 framework for U.S. G-SIBs. o Established quarterly monitoring process which included regular meetings with bank senior management to review drivers of changes in market RWA, monitor model performance, and evaluate risk management practices.

Kroll Bond Rating Agency, New York, New York October 2010-December 2011 Head Quantitative Analyst

Responsible for the management, development, and validation of multiple models that were employed in the issuance of ratings for asset backed securitizations, monoline bond portfolios, and financial institutions.

• Team lead for the research, design, and construction of robust statistical models which assessed the credit risk and financial strength of U.S. domestic banks, savings and loans, and credit unions.

• Utilizing mathematical expertise, validated and critically tested the strengths and limitations of stochastic based, market risk models for bond and portfolio insurers.

• Developed Monte Carlo simulations to quantify enhancement levels associated with the ratings and tranching of CMBS CDOs and other structured ABS products.

• Led the validation of statistical based models utilized in the ratings of RMBS securitizations. Authored technical research publications and rating methodologies. J.P. Morgan Chase Manhattan Bank, New York, New York May 1999-March 2003 Chase Insurance Group, Statistician

Utilizing statistical based data mining techniques with SAS on large financial datasets, our team created a variety of predictive and response models to examine the consumer and market behavior of the insurance industry.

• Worked with actuaries in researching financial and economic variables impact annuity rates of an external vendor model. Incorporating the Federal Reserve Treasury Rates and proprietary bank data, developed AR- models to forecast annuity rates.

• Applying logistic regression as the primary response-modeling tool, increased response rates for marketing campaigns by as much as 150%. Interacted with senior marketing and database management from other bank groups to coordinate database strategies and marketing efforts. ACADEMIC EXPERIENCE

Hunter College, CUNY, New York, New York August 2006-Present Department of Mathematics and Statistics, Adjunct Assistant Professor Consolidating my knowledge of doctoral courses to the classroom environment and leveraging strong communication and presentation skills, I have been an active faculty member in the department of mathematics and statistics.

• Teach a variety of undergraduate math courses and graduate mathematic and statistic courses.

• Taught Python, R, SAS, Matlab, and S-Plus programming languages within course curriculums’.

• Advised several master’s thesis and mentored undergraduate research projects. EDUCATION

Graduate Center, City University of New York, New York, New York Doctoral Degree in Mathematics

(Specialization in applied probability theory and stochastic analysis) NYU Polytechnic Institute, New York, New York

Masters of Science in Mathematics

Belmont University, Nashville, TN

Bachelor of Business Administration, Management Science and Accounting

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