Senior Quantitative Analyst (R/ R Shiny)

Crisil; Maharashtra; Hybrid; INR 2,800,000 - 4,200,000; Full time

Job summary

Crisil seeks a Senior Quantitative Analyst to own end-to-end model development using R Shiny. You will craft production-grade apps, understand complex ratings criteria, and leverage GenAI to boost coding efficiency and documentation quality.

The role requires rapid learning, strong autonomy, and ability to manage multiple high-priority projects in a hybrid Pune setting.

Qualifications

• Advanced degree in a quantitative discipline such as Statistics, Mathematics, Financial Engineering, or Computer Science.
• Proven ability to implement GenAI tools into the SDLC.
• Strong background in R programming and production-grade Shiny apps.

Responsibilities

• Lead end-to-end model development and deployment using the R Shiny framework.
• Develop high-performance Shiny applications translating complex rating models for stakeholders.
• Incorporate Generative AI to accelerate coding, debugging and documentation.
• Independently manage multiple high-priority workstreams with minimal guidance.
• Ensure robust testing and governance for models and apps.

Skills

R programming

R Shiny

Generative AI

Git

CI/CD

Python

Education

Advanced degree in quantitative field

Tools

R Shiny

Python

CI/CD pipelines

Git

Job description

Job Description: Senior Quantitative Analyst

Location:Pune (Hybrid)Department:MethodologiesReports to:Sector Lead

Role Summary

We are seeking a highly skilled and proactive Senior Quantitative Analyst to join our team. The successful candidate will own the end-to-end model development value chain using R Shiny, while simultaneously mastering our complex Ratings criteria and existing model portfolio. We are looking for a forward-thinking professional who doesn't just follow existing workflows but actively leverages cutting-edge technologies—including Generative AI (GenAI)—to accelerate coding, optimize workflows, and enhance analytical output. This role requires a high degree of autonomy, rapid learning agility, and the ability to manage complex, multi-faceted projects with minimal supervision.

Key Responsibilities

• End-to-End Model Lifecycle Management:Lead all activities across the model development value chain, including model architecture, development, rigorous testing, validation, and deployment using the R Shiny framework.
• Domain Mastery & Upskilling:Quickly acquire and apply deep subject matter expertise in our proprietary Ratings criteria and existing model portfolio to ensure all technical outputs are contextually accurate and aligned with business standards.
• Advanced Tool Development:Design and deploy sophisticated, high-performance R Shiny applications that translate complex credit rating models into intuitive tools for stakeholders.
• AI-Augmented Development:Proactively leverage Generative AI capabilities (e.g., LLMs for code generation, debugging, and documentation) to increase development velocity, improve code quality, and automate repetitive programming tasks.
• Autonomous Project Leadership:Independently manage multiple high-priority workstreams, prioritizing tasks effectively and delivering high-quality results under minimal guidance from management.
• Quality Assurance & Governance:Implement robust testing frameworks to ensure the integrity of models and applications, ensuring they meet all internal governance and regulatory standards.

Required Skills & Qualifications

• Technical Mastery:Expert-level proficiency inR programmingand extensive experience building production-grade applications inR Shiny.
• GenAI Proficiency:Proven ability to integrateGenerative AI toolsinto the software development lifecycle (SDLC) to enhance coding efficiency, troubleshooting, and technical documentation.
• Rapid Learning Agility:A demonstrated track record of quickly mastering complex, specialized domain knowledge (e.g., financial methodologies, regulatory frameworks, or proprietary rating logic).
• Quantitative Background:Advanced degree (Bachelors or Masters preferred) in a quantitative discipline such as Statistics, Mathematics, Financial Engineering, or Computer Science.

Additional Qualifications

• Experience inCredit Ratings, Risk Management, or Financial Modelingwithin a global financial institution.
• Proficiency with version control (Git) and automated deployment pipelines (CI/CD).
• Knowledge of Python or other languages used in conjunction with R for machine learning workflows.