Gen AI Credit Risk Analyst and Modeler

Tiger Analytics is a global leader in AI and analytics, helping Fortune 1000 companies solve their toughest challenges. We offer full-stack AI and analytics services & solutions to empower businesses to achieve real outcomes and value at scale. We are on a mission to push the boundaries of what AI and analytics can do to help enterprises navigate uncertainty and move forward decisively. Our purpose is to provide certainty to shape a better tomorrow. Our team of 4000 technologists and consultants are based in the US, Canada, the UK, India, Singapore, and Australia, working closely with clients across CPG, Retail, Insurance, BFS, Manufacturing, Life Sciences, and Healthcare. Many of our team leaders rank in the Top 10 and 40 Under 40 lists, exemplifying our dedication to innovation and excellence. We are a Great Place to Work-Certified ), recognized by analyst firms such as Forrester, Gartner, HFS, Everest, ISG and others. We have been ranked among the ‘Best’ and ‘Fastest Growing’ analytics firms lists by Inc., Financial Times, Economic Times and Analytics India Magazine. About the role Tiger Analytics is seeking a Gen AI Credit Risk Analyst and Modeler to join our Banking and Financial Services practice within the Credit Risk team. This role is suited for professionals with strong credit risk domain expertise and hands-on analytical skills, who can also leverage Generative AI and Large Language Models (LLMs) to enhance credit risk analysis, automation, and decision-support workflows. The role requires close collaboration with business, risk, and compliance stakeholders, translating complex credit risk requirements into scalable analytical and GenAI-enabled solutions. The individual is expected to demonstrate strong technical depth, business acumen, and leadership qualities. Responsibilities: Design, develop, and maintain credit risk models across the credit lifecycle including acquisition, underwriting, portfolio monitoring, and loss forecasting. Apply statistical and analytical techniques to solve complex credit risk problems across banking portfolios. Leverage GenAI and LLMs , including prompt engineering, to support risk analysis, automation, documentation, and insight generation. Work closely with business and risk stakeholders to understand requirements and translate them into implementable analytical and GenAI-based solutions. Develop, validate, and document credit risk models in line with regulatory and internal governance standards. Support credit risk strategy initiatives such as scorecard development, policy analytics, segmentation, and portfolio optimization. Communicate analytical insights, model results, and GenAI outputs clearly to both technical and non-technical audiences. Provide technical guidance and mentorship to junior team members. Required Qualifications, Capabilities and Skills: Postgraduate degree in Economics, Statistics, Mathematics, Data Science, or a related quantitative discipline. 5 years of experience in Credit Risk analytics or modeling within the banking or financial services domain. Strong understanding of credit risk concepts including PD, LGD, EAD, underwriting, scorecards, and portfolio risk. Hands-on experience with Python and SQL for data extraction, modeling, and analytics. Practical understanding of Generative AI / LLMs, including prompting techniques and applied business use cases. Strong communication and stakeholder management skills, with the ability to engage business, risk, and technology partners. Strong problem-solving mindset with the ability to structure ambiguous business problems. Desired Experience: Experience in AML, Fraud, or Risk analytics within a banking environment. Exposure to regulatory frameworks such as Basel, IFRS9, CECL , or model risk governance. Experience integrating traditional credit risk models with GenAI-driven workflows. Prior experience in a consulting or client-facing analytics role. Desirable Skills: Experience deploying analytics or GenAI solutions into production environments. Ability to translate complex analytical concepts into clear, actionable business recommendations. Strong ownership, leadership mindset, and ability to drive initiatives end-to-end. Curiosity and continuous learning attitude, especially in evolving AI and risk analytics areas.