Job
Description
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. Driven by curiosity, you are a reliable, contributing member of a team. In our fast-paced environment, you are expected to adapt to working with a variety of clients and team members, each presenting varying challenges and scope. Every experience is an opportunity to learn and grow. You are expected to take ownership and consistently deliver quality work that drives value for our clients and success as a team. As you navigate through the Firm, you build a brand for yourself, opening doors to more opportunities. Skills Examples of the skills, knowledge, and experiences you need to lead and deliver value at this level include but are not limited to: Apply a learning mindset and take ownership for your own development. Appreciate diverse perspectives, needs, and feelings of others. Adopt habits to sustain high performance and develop your potential. Actively listen, ask questions to check understanding, and clearly express ideas. Seek, reflect, act on, and give feedback. Gather information from a range of sources to analyse facts and discern patterns. Commit to understanding how the business works and building commercial awareness. Learn and apply professional and technical standards (e.g. refer to specific PwC tax and audit guidance), uphold the Firm's code of conduct and independence requirements. 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
