Model Risk · Model Validation · Model Development
I build, challenge, and defend the models banks run on.
Ten years in quantitative finance, eight in model risk — across credit, market, treasury and liquidity risk. I win regulatory approvals for new models, challenge the ones already running, and increasingly build the tooling and AI that do it faster.
FRM · CQF · 8+ YRS MODEL RISK · PYTHON · R · C++
01 — Expertise
Across the model lifecycle
Building, challenging, and winning regulatory approval for models across credit, market, treasury and liquidity risk.
D.01
Credit Risk Models
PD, LGD and loss models — IRB and Pillar I/II, CCAR stress testing, and securities-backed lending — validated and taken through regulatory approval.
PD · LGD · IRB · CCAR
D.02
Market Risk Models
VaR, SVaR and RNiV across FX, credit and structured products — as lead reviewer presenting findings to regulators and senior risk committees.
VaR · SVaR · RNiV
D.03
Treasury & Liquidity Risk
Interest-rate risk in the banking book, liquidity and funding risk, and firm-wide risk models.
IRRBB · Liquidity · Funding
D.04
Validation & Challenge
Independent review end to end: conceptual soundness, benchmarking, replication and outcomes analysis — the testing that decides whether a model can be trusted.
Challenge · Testing
D.05
Regulatory Approval & Governance
New-model approvals, ongoing regulatory engagement, audit readiness, and regulatory deliverables including NFA submissions.
Approvals · Governance
D.06
Model Development & AI
I build, not just review: production Python, R and C++, plus AI automation for model risk — including a RAG agent that drafts tailored validation scope.
Python · R · AI · RAG
10+ yrs
Quantitative finance
8+ yrs
In model risk
FRM · CQF
Certifications
Py · R · C++
Production code
02 — Blog
Blog Posts
Technical notes from pythonandr.com — the maths, worked in code.
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Incident Report: A Risk Quant Reviews DeepLearning.AI’s Agentic AI Course
A model risk quant reviews DeepLearning.AI’s five-module Agentic AI course — module by module, lab by lab — benchmarked against Hugging Face, LangGraph, CrewAI and more. Great ideas, honest teaching, demo-grade code.
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When the Skeptic Reads the Manual: A Risk Quant Learns to Build AI Agents
In which I build the perfect AI-course reading list on DeepLearning.AI, then do to it what I’m paid to do to everyone else’s models — validate it, starting with how old the courses actually are.
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Scraping the Daily India Covid-19 Tracker for CSV Data
Turning a live public dashboard into clean, analysis-ready data — pragmatic data engineering in Python.
03 — About
About
I’m Anirudh Jayaraman — a model risk quant with over a decade in quantitative finance and more than eight years in model risk, spanning credit, market, treasury interest-rate, and liquidity & funding risk. I’m currently an Associate Director at UBS, and I’ve held model-risk roles at Morgan Stanley, MSCI and CRISIL.
I understand models well enough to work either side of them — to challenge one as an independent reviewer, or to build it. I’ve won regulatory approvals for new models, owned regulatory market-risk deliverables, presented findings to regulators and senior risk committees, and validated PD, LGD, VaR, SVaR and RNiV models across asset classes.
I’m FRM and CQF certified and code fluently in Python, R and C++. Increasingly I build the tooling too: I lead AI-automation streams in the model-risk function and built a RAG agent that drafts tailored model-validation scope. pythonandr.com is where I work through the statistics and econometrics behind the models, in code.
I’m open to senior model-risk and model-development roles where deep quantitative work, regulatory fluency, and clear written challenge all matter at once.
Get in touch
Reviewing a model — or a candidate?
Happy to talk model risk, validation, model development, or a role. The fastest way to reach me is email or LinkedIn.
Anirudh Jayaraman 