Credit Quant
Company: Weekday AI
Role summary
Credit Quant is a full-time role at Weekday AI (remote). Posted 2026-08-31. Experience level: 5-10.
- Job title
- Credit Quant
- Company
- Weekday AI
- Location
- Remote
- Work arrangement
- remote
- Employment type
- full-time
- Category
- Software Engineering
- Experience level
- 5-10
- Date posted
- 2026-08-31
This is a fantastic opportunity to join a cutting-edge AI company focused on credit analysis. As a Credit Quant, you'll work remotely, enjoying the flexibility of a full-time role while contributing to innovative financial solutions. It's a great chance to grow your career in a dynamic and forward-thinking environment.
Job description
This role is for one of Weekday’s clients
Salary range: Rs 4000000 - Rs 7000000 (ie INR 40 - 70 LPA)
Min Experience: 4+ years
Location: India
JobType: full-time
We are seeking an experienced Credit Quant to develop and enhance mathematical models, pricing engines, and risk analytics for credit financial instruments, including CDS, loans, and structured credit products. This role combines hands-on front-office quantitative modelling with close collaboration across Risk, Front Office, Technology, and Model Risk Management teams.
The successful candidate will work as part of a global Quant Analytics and Quant Development function, partnering closely with international quantitative teams and supporting sophisticated credit trading and risk management activities.
Key Responsibilities Design, develop, and maintain pricing and valuation models for flow credit products and structured credit derivatives, including CDS, loans, repo, CLNs, and CDOs.
Build and enhance quantitative risk analytics frameworks covering market, credit, and counterparty risk.
Develop pre-trade analytics, relative-value measures, and quantitative tools to support and optimise trading decisions.
Analyse large volumes of market and reference data to identify trends, generate trading signals, and improve model calibration.
Implement advanced quantitative models within high-performance C++ analytics libraries and take ownership of production-quality code.
Collaborate with traders, risk managers, technology teams, and model validation functions to ensure effective implementation and adoption of quantitative solutions.
Ensure models and analytics comply with internal risk frameworks, governance standards, and applicable regulatory requirements.
Support model validation, documentation, audit activities, and ongoing enhancements to quantitative methodologies.
Research new quantitative techniques and market developments to improve pricing, risk measurement, and trading analytics.
Required Skills & Experience Strong understanding of credit markets, stochastic calculus, probability theory, numerical methods, and quantitative finance.
5+ years of experience in front-office investment banking, asset management, or buy-side environments, with a focus on fixed income or credit markets.
Strong programming skills in C++ and/or Python, with hands-on experience implementing quantitative models in production environments.
Strong mathematical, analytical, and problem-solving abilities.
Experience developing pricing, valuation, risk, or trading models for credit or fixed-income instruments.
Ability to work effectively with Front Office, Risk, Technology, and Model Risk Management stakeholders.
Excellent interpersonal and communication skills, with the ability to clearly explain complex quantitative concepts to both technical and non-technical audiences.
Ability to present analysis, modelling approaches, and recommendations clearly to senior stakeholders.
Education Bachelor’s or Master’s degree in Mathematics, Statistics, Physics, Computer Science, Engineering, or another STEM discipline from a strong academic institution.
Candidates with degrees from leading engineering and technical institutes are preferred.
Good-to-Have Skills Python
Advanced quantitative modelling
Credit derivatives
Fixed-income analytics
C++ quantitative development
Risk modelling and model validation
Electronic trading and pre-trade analytics