Mohammed Mhaidra | Quantitative Finance & Model Risk

mhaidra@qantify:~

Core Pillars

Ĥψ = Eψ

dS = rSdt + σSdW

L = -Σ y·log(p)

CORE

Quantitative Finance & Risk

Quant with experience in modeling and validation of complex derivatives pricing, market risk frameworks, and stress-testing models. Expert in stress testing and capital adequacy benchmarks.

Cross-Domain Connections:

Modelling and ValidationDerivatives, VaR and PPNR models

Stress Testing (CCAR/ICAAP)Modeling and validating Economic Capital models and PPNR forecasting models to ensure legal entities remain resilient under severe macroeconomic shock scenarios.

Model GovernanceFormulating rigorous critiques of limitations, model assumptions, and parameter sensitivities for global regulators.

Experience

VP, Modeling/ Analysis/ Validation Officer

Citi

Sep 2025 -- Present

Stress testing across LATAM, APAC, and MEA for Traded Market Risk portfolios using hybrid and scaled VaR models, Counterparty Credit Risk, and Wholesale Credit Risk.
Developed Gen AI solutions to automate model-related workflows, analysis, and reporting.

Senior Model Risk Specialist (VP)

BNY

Jun 2024 -- Jul 2025

Led end-to-end model validation for a portfolio of ~30 complex models across global domains, focusing on forecasting (PPNR) models for CCAR frameworks and Economic Capital models for ICAAP.
Designed and executed quantitative testing: comprehensive methodology assessments, statistical benchmarking, sensitivity analysis, and outcome reasonableness checks.
Participated directly in Federal Reserve Board (FRB) examination meetings and executive capital/stress oversight forums, presenting model logic and remediation plans.
Guided, mentored, and upskilled a team of three reviewers across multiple complex workstreams.

Model Risk Specialist (VP)

BNY

Jan 2022 -- Jun 2024

Validated pricing models for complex, multi-asset products including Interest Rate, Foreign Exchange, and Credit (IR/FX/Credit) derivatives, CMS, equity, and CCY swaps, swaptions, caps/floors, inflation swaps, FX swaps/forwards, CDS/CDX, and FRAs.
Assessed high-frequency model performance utilizing historical stress testing, dynamic scenario analysis, and backtesting diagnostics.
Reviewed forecasting and liquidity models for CCAR, evaluating structural robustness under adverse macroeconomic scenarios.
Evaluated climate risk models for corporate and insurance portfolios, challenging assumptions and data limitations.
Supported London Interbank Offered Rate (LIBOR) transition workstreams, executing model change impact analysis.
Implemented an automated multi-instrument pricing engine in Python (QuantLib) to benchmark desk/vendor pricing, reducing validation cycle times by ~20-30%.

Model Risk Specialist

BNY

Jul 2021 -- Sep 2021

Reviewed and validated global curve models by analyzing mathematical modeling frameworks, implementation methodologies, and underlying statistical assumptions.

Ph.D. Visiting (Particle Physics)

University of Bristol

Jan 2020 -- Jun 2021

Conducted advanced Monte Carlo simulations and predictive statistical analysis on massive experimental datasets.
Strengthened lifelong skills in formal logical reasoning, rigorous hypothesis testing, and explaining highly complex technical models with extreme precision.

Research Assistant

Warsaw University of Technology

May 2018 -- Nov 2021

Supported top-tier academic research involving quantitative modeling methods, heavy computational analysis, and detailed technical reporting.

Recent Projects

Quant FinanceJan 2026

Designing-Market-Shock-Scenarios

Re-implementation of macroeconomic and asset-pricing stress tests (CCAR/ICAAP benchmarks), pricing portfolio losses under extreme shock scenarios.

Stress TestingCCAR/ICAAPMarket Risk

View Codebase →

Quant FinanceAug 2025

Portfolio-Analytics-Dashboard

A comprehensive investment dashboard designed to parse, analyze, and visualize multi-asset portfolio statements (specifically styled for Wafabourse statement data).

Finance DevPythonData Analytics

View Codebase →

View More Projects on GitHub

Publications

📰 Progress in Nuclear Energy

Vol. 186, Art. 105803•Citations: 1•2025

Progress in Nuclear Energy

Vol. 186, Art. 105803 (2025)

3D imaging of gas bubbles in nuclear waste containers via Muon Scattering Tomography

M. Mhaidra, A. Alrheli, D. Barker...

Abstract:

This study presents a non-destructive method for detecting and sizing gas bubbles in radioactive waste containers. We utilize cosmic muon scattering tomography (MST) to reconstruct 3D voxelized density profiles. Computational results validate that the proposed algorithms can resolve bubbles as small as 5cm in diameter within concrete matrices, mitigating gas accumulation risks.

PDF

3D imaging of gas bubbles in nuclear waste containers via Muon Scattering Tomography

Authors: M. Mhaidra, A. Alrheli, D. Barker, C. De Sio, D. Kikoła, A. Kopp, P. Stowell, et al.

Open Paper Link

Google Scholar Profile

Education

Ph.D. in Applied Nuclear Physics

Warsaw University of Technology

M.S. in Particle Physics

University of Cagliari

B.S. in Physics

University of Mohammed V

Expertise & Skills

Quantitative Expertise

Risk Modelling

DerivativesMarket RiskCredit RiskCounterparty Credit Risk

Stress Testing & Regulatory

Framework reviewsScenario adequacyCCARICAAPFit-for-purpose assessmentModel overlays

Model Governance

Effective challengeAssumption reviewSensitivity testingBenchmarkingDocumentationMonitoring

Technical Stack

Data Science & Modeling

PythonRC & C++QuantLibPandasNumPyScikit-learnSQLMatlabTime-series Forecasting

AI, Automation & Data Engineering

LLM API IntegrationGenAI WorkflowsAPI IntegrationData ScrapingNetworkingGit

Infrastructure & Methodologies

DockerLinux/BashCloud Readiness

Let's work together

Seeking data-driven risk management solutions, custom derivatives pricing engines, or automated GenAI pipelines? Let's connect.