Quant Risk Analyst - London - Artificial Risk Intelligence

Description

Quantitative Risk Analyst - Dynamic Hedging


Company:
Artificial Risk Intelligence


Location:
London, United Kingdom


Department:
Risk Analytics & Dynamic Hedging

Reports To:
Head of Financial Products


Employment Type:
Full-time, Permanent, Flexible/Remote

Position Overview

ARi is seeking an exceptional Quantitative Risk Analyst to join our expanding team and play a pivotal role in structuring our Dynamic Hedging capability.

As a leading AI-powered risk management platform serving enterprise banks and financial institutions, we're revolutionising how organisations manage credit and operational risk through analytics and advanced machine learning.

This role offers the unique opportunity to work at the intersection of quantitative finance, artificial intelligence, and risk management, building next-generation hedging strategies for complex financial portfolios.

ARi is partnered with a $17 Billion a year technology enterprise powerhouse. ARi solves critical Credit and Loan problems within global enterprise banking institutions. Increasing liquidity and stopping the haemorrhaging of cash.

Compensation Package


Base Salary:
£85,000 per annum


Performance Bonus:
Discretionary annual bonus


Equity Participation:
Share options


Total Package Value:
Up to £110,000+ with equity upside

Key Responsibilities

Dynamic Hedging Strategy Development
Design, develop, and implement dynamic hedging strategies for credit portfolios, fixed income instruments, and loan books
Build quantitative models for hedge ratio optimisation and rebalancing frequency determination
Develop risk-neutral valuation frameworks for complex derivative structures
Create portfolio immunisation strategies against interest rate, credit spread, and prepayment risk

Credit & Loan Risk Analytics
Conduct advanced credit risk modelling including probability of default (PD), loss given default (LGD), and exposure at default (EAD)
Develop credit valuation adjustment (CVA) and debt valuation adjustment (DVA) frameworks
Model correlation structures within credit portfolios for risk aggregation

Fixed Income Quantitative Analysis
Construct yield curve models and duration/convexity analysis frameworks
Develop option-adjusted spread (OAS) models for mortgage-backed securities and structured products
Build interest rate risk models for both trading and banking books
Implement fixed income derivatives pricing models (swaptions, caps, floors, CDS)

Model Development & Implementation
Translate quantitative research into production-ready Python code
Integrate techniques into traditional risk models to enhance predictive accuracy
Develop backtesting frameworks to validate model performance
Implement stress testing and scenario analysis capabilities

Risk Reporting & Communication
Produce detailed risk reports for internal stakeholders and clients
Present complex quantitative concepts to non-technical audiences
Collaborate with tech teams to understand hedging requirements
Support regulatory reporting requirements (Basel III/IV, IFRS 9, Solvency II)

Essential Requirements

Education & Qualifications
PhD preferred in Mathematics, Physics, Engineering, Financial Engineering, or related quantitative discipline
Master's degree minimum in quantitative field
Professional certifications (FRM, CQF, PRM) are advantageous

Technical Experience
Minimum 5 years experience in financial asset risk management

Mandatory:
Deep expertise in Credit and Loans risk management

Mandatory:
Extensive Fixed Income experience (non-negotiable requirement)

Essential:
Python programming (NumPy, Pandas, SciPy, scikit-learn)
Strong understanding of derivative pricing (Black-Scholes, stochastic calculus, PDE methods)
Experience with Monte Carlo simulation and numerical methods

Industry Background
Front office experience from sell-side (investment bank) or buy-side (hedge fund, asset manager)
Banking risk management experience in credit risk, market risk, or ALM
Hedge fund or proprietary trading experience with demonstrable P&L responsibility
Direct experience in dynamic hedging execution and management

Artificial Intelligence & Machine Learning
Basic understanding of AI/ML concepts and applications in finance
Familiarity with supervised and unsupervised learning techniques
Experience applying ML to credit scoring, default prediction, or portfolio optimisation preferred
Knowledge of gradient boosting, random forests, neural networks advantageous

Technical Skills Matrix

Must Have (Non-Negotiable)
Python (advanced proficiency with quantitative libraries)
Fixed Income analytics and derivatives
Credit Risk modeling (PD, LGD, EAD)
Loan portfolio analytics
Dynamic hedging strategies
Stochastic calculus and derivatives pricing

Highly Desirable
R, MATLAB, or Julia programming
SQL and database management
Bloomberg Terminal, Reuters Eikon
C++ or C# for performance-critical applications
Cloud computing (AWS, Azure, GCP)
Version control (Git, GitHub)
TensorFlow, PyTorch, or similar ML frameworks
Preferred Experience

Industry Sectors
Banking (universal, investment, commercial)
Hedge funds (multi-strategy, fixed income arbitrage, credit)
Asset management (fixed income, credit, structured products)
Fintech and risk technology platforms

Personal Attributes

Continuous Learning
Passion for staying current with academic research and industry developments
Intellectual curiosity about AI/ML applications in risk management
Proactive approach to professional development

Why Join ARi? - Be The Envy of Your Peers.

Impact & Ownership
Direct influence on company direction and product roadmap
Equity stake in a high-growth fintech company
Opportunity to shape the future of risk management

Professional Growth
Collaborate with world-class quantitative researchers and engineers
Access to the latest tools, data, and computational resources
Clear path to senior leadership roles as the company scales

Company Culture
Intellectually rigorous environment that values technical excellence
Flat organisational structure with direct access to leadership
Flexible working arrangements with focus on outcomes

Application Process


Stage 1:
Initial Screening

CV and cover letter review
Technical skills assessment
Academic credentials verification


Stage 2:
Technical Interviews

Quantitative Interview:
Mathematical problem-solving and derivatives pricing

Programming Challenge:
Python coding assessment

Case Study:
Credit portfolio hedging strategy presentation


Stage 3:
Final Interviews

Technical Deep Dive:
Panel interview with quant team

Cultural Fit:
Conversation with founders and leadership team


Stage 4:
Offer

Offer letter and equity documentation
Onboarding preparation

Expected Timeline: 3-4 weeks from application to offer

How to Apply

Please submit the following to

Curriculum Vitae (PDF format)

Cover Letter addressing:
Your experience in credit/loans and fixed income
Specific examples of dynamic hedging strategies you've developed
Your interest in AI/ML applications to risk management
Why you're excited about ARiRisk Management

Code Sample (optional but highly valued):
Python implementation of a risk model or hedging strategy
GitHub repository link acceptable

Application Deadline:
Rolling basis (immediate interviews for exceptional candidates)

Contact:
For inquiries, please email with subject line "Quant Risk Analyst - Dynamic Hedging Query"

Equal Opportunity Statement

ARi is an equal opportunity employer committed to building a diverse and inclusive team. We welcome applications from all qualified candidates.