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Quantitative Researcher with less than a year in financial modeling, data analytics, and AI-augmente
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Dynamic and results-oriented professional with a strong foundation in financial risk management and quantitative analysis. Skilled in developing sophisticated models for market and credit risk, utilizing advanced statistical techniques, and deploying AI-driven financial web applications. Proven ability to translate complex data into actionable insights for strategic decision-making and risk mitigation.
Shaheed Sukhdev College of Business Studies (University of Delhi)
Bachelor of Management Studies (Finance) · Finance
January 1, 2022 – January 1, 2025
S.S Mota Singh School (New Delhi)
AISSCE · Commerce
January 1, 2020 – January 1, 2022
S.S Mota Singh School (New Delhi)
AISSE
January 1, 2018 – January 1, 2020
BB Advisory
Finance Intern
July 1, 2024 – September 1, 2024
India
VASICEK AND CIR SHORT-RATE MODELS
June 1, 2026 – June 1, 2026
Replicated and extended a 2007 Vasicek vs. CIR short-rate study across 30 years and 3 countries: CIR is more stable on average (CV 74-83% vs. 158–251%) but Vasicek wins in every crisis tested a regime-dependent reversal the original study's sample window likely masked. Compared two calibration methods — curve-fitting vs. time-series estimation — finding curve-fitting gives tighter pricing fits while time-series holds parameters far more stable, a trade-off that should drive method choice by pricing vs. stress-testing use case.
CREDIT RISK SQL ANALYSIS
June 1, 2026 – June 1, 2026
Modelled Expected Loss by loan grade using multi-CTE SQL queries: per-loan expected loss ranged from $503 (Grade A) to $6,169 (Grade G), exposing a 12× risk differential across the credit spectrum. Segmented 1.34M borrowers by FICO and DTI bands: sub-700 FICO borrowers with DTI above 20 in Grades D-G defaulted at 39.18%, nearly double the 19.96% portfolio baseline, pointing to specific underwriting criteria worth tightening.
CREDIT RISK ENGINE
May 1, 2026 – May 1, 2026
Built a Vasicek Monte Carlo engine across 500 obligors; stress-tested combined correlation and default-rate shocks, observing Expected Shortfall increases exceeding 80% versus baseline — exposing how capital buffers calibrated under calm assumptions can materially underestimate losses when both risk factors move together; deployed at credit.karamfrm.com. Priced CDO tranches via Gaussian Copula across an equity/mezzanine/senior waterfall; found senior tranche expected loss near-zero (0.04%) at baseline correlation — the same subordination assumption regulators and rating agencies relied on before 2008, when realized correlation departed sharply from those estimates.
CROSS-ASSET CORRELATION TERMINAL
April 1, 2026 – April 1, 2026
Built a live cross-asset correlation terminal tracking 18 instruments across FX, equities, rates, and commodities with Pearson/Spearman/Kendall matrices, PCA regime detection (AR1), and Granger causality; found that the correlation between Gold (XAU/USD) and AUD/USD collapsed from +0.93 (current) to +0.17 during the 2008 GFC on 152 daily observations, exposing that commodity-FX pairs assumed to co-move as diversifiers can decouple entirely under contagion, leaving correlation-based VaR estimates structurally understated; deployed at corr.karamfrm.com.
TAIL RISK ANALYZER
March 1, 2026 – March 1, 2026
Engineered a tail risk platform revealing that a diversified equity portfolio's worst-1% scenario is 2.86× its worst-10% scenario three methods (Historical, Parametric, Monte Carlo / 10,000 simulations) converging on a 1.46× ES/VaR ratio, quantifying the capital underestimation gap that caused systemic failures in 2008 when institutions relied on VaR alone; deployed at var.karamfrm.com.
FRM Part-1
GARP
January 1, 2026 – Present
FIFA Rankings vs ELO Ratings: Predictive Validity in World Cup Knockout Stages
SSRN
January 1, 2026 – Present
Advanced Microsoft Excel
Udemy
January 1, 2023 – Present
Equity Market Analyst
Finlatics
January 1, 2023 – Present
Cultural Fit Analysis
The candidate's diverse range of personal and academic projects, all centered around quantitative finance and risk management, aligns well with a Quantitative Researcher role. The proactive pursuit of certifications (FRM Part-1) and independent research demonstrates a strong drive for continuous learning and a deep passion for the field. The projects show a critical thinking approach, often challenging conventional assumptions (e.g., VaR limitations, correlation decoupling), which is valuable in a research-oriented environment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong analytical mindset, problem-solving capabilities, and an ability to articulate complex financial concepts. The deployment of personal projects suggests initiative and a results-oriented approach. While direct team collaboration experience is not explicitly detailed beyond project descriptions, the nature of quantitative research often requires independent work alongside collaborative efforts.