Job
Description
PwC is one of the most prestigious professional services firm in the world, serving as the auditor to nearly half of the world’s largest banks. PwC US Risk and Regulatory (R&R) comprises of a highly experienced team of risk management specialists supporting global financial institutions in their risk management initiatives. R&R has significant exposure to, and driver of, industry leading practices and has deep knowledge of regulatory expectations. R&R professional’s experience covers all financial model types, including those used to manage credit risk, market risk, operational risk and compliance risk—as well as those used for financial reporting, valuations and economic capital estimation. Risk Analytics Center of Excellence (CoE) is the India extension of R&R practice and provides key risk analytics services to global banks, investment firms, and asset management entities. It comprises of risk analytics professionals with stellar quantitative pedigree from premier institutions, industry certifications in CFA, FRM, PRM etc. and proven professional credentials in risk modeling and analytics at reputed financial institutions and consulting firms. As an integral part of PwC US R&R, Risk Analytics CoE drives risk analytics engagements, opportunity pursuits and cutting-edge innovation using data science, Artificial Intelligence, Machine Learning and Deep Learning. 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. Required Skills & Experience 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. Education & Certifications 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 Show more Show less
