Gen AI Credit Risk - Senior Associate - Bangalore

At PwC, our people in data and analytics focus on leveraging data to drive insights and make informed business decisions. They utilise advanced analytics techniques to help clients optimise their operations and achieve their strategic goals. In data analysis at PwC, you will focus on utilising advanced analytical techniques to extract insights from large datasets and drive data-driven decision-making. You will leverage skills in data manipulation, visualisation, and statistical modelling to support clients in solving complex business problems.

Focused on relationships, you are building meaningful client connections, and learning how to manage and inspire others. Navigating increasingly complex situations, you are growing your personal brand, deepening technical expertise and awareness of your strengths. You are expected to anticipate the needs of your teams and clients, and to deliver quality. Embracing increased ambiguity, you are comfortable when the path forward isn’t clear, you ask questions, and you use these moments as opportunities to grow.

AI/ML & Credit Risk Analytics Professional – Job Specification

Candidate would be responsible for developing, validating, auditing, and maintaining AI/ML-powered credit risk models. Candidates would be expected to support financial institutions in meeting jurisdictional regulatory requirements and their broader risk management initiatives.

Multiple positions required;

Experience level: 2–6 years of relevant experience

Location: Bangalore

Role Overview

We are looking for high-caliber professionals with strong foundations in credit risk modeling and hands-on experience in AI/ML techniques. The ideal candidate will contribute to the development and validation of regulatory and strategic risk models, while also applying machine learning and generative AI techniques to enhance model accuracy, efficiency, and interpretability.

Key Responsibilities

• Develop, validate, and document credit risk models (PD, LGD, EAD) for retail and wholesale portfolios across regulatory (CECL, IFRS 9, Basel) and business-use contexts.
• Apply AI/ML algorithms (e.g., LightGBM, XGBoost, Random Forest, Neural Networks) to improve prediction power and model performance beyond traditional approaches.
• Implement Generative AI and LLM-based applications using RAG pipelines, document intelligence, and model documentation automation. Experience with agentic frameworks like Autogen, LangChain, or similar would be helpful.
• Experience of development and deployment of models in cloud-based platforms such as Azure, AWS, GCP etc.
• Design explainable AI solutions by incorporating techniques like SHAP, LIME, and feature attribution methods to enhance transparency in high-stakes modeling environments.
• Partner with cross-functional teams, including business stakeholders, technology teams, and model governance, to ensure model alignment with business objectives and regulatory expectations.
• Contribute to innovation initiatives and support proposal development, thought leadership, and solution architecture in the AI/ML space.
  
  

• 2–6 years of total experience, with minimum 2 years in AI/ML or GenAI model development or validation.
• Strong understanding of credit risk modeling frameworks, scorecard development, and risk metrics (e.g., RWA, Expected Loss, Economic Capital).
• Proficient in Python and SQL, with hands-on experience using ML libraries such as scikit-learn, Tensorflow, Pytorch and transformer-based LLM packages
• Familiarity with regulatory standards such as CECL, IFRS 9, CCAR/DFAST, Basel II/III, SR 11-7, and model governance best practices.
• Exposure to cloud environments (Azure preferred), version control (Git), and workflow automation tools.
• Experience with credit bureau data, vendor models (e.g., FICO, Moody’s, S&P), and financial benchmarking is a plus.
• Ability to clearly communicate complex technical content to non-technical stakeholders through reports, dashboards, and presentations.
  
  

• Master’s degree or higher in Statistics, Mathematics, Economics, Data Science, Engineering, or Finance.
• Professional certifications such as FRM, CFA, CQF, or in product management equivalent are preferred.
• Contributions to opensource AI / ML projects and competitions is preferred