5 Credit Modeling Jobs

Job Title: Lead Quant Analyst, Credit Quants About the Team: DBRS Morningstar Credit Ratings, LLC is registered with the U.S. Securities and Exchange Commission as a nationally recognized statistical rating organization (NRSRO). DBRS Morningstar Credit Ratings issues credit ratings on a variety of security types including corporate and structured finance securities. This Morningstar subsidiary aims to increase market transparency by providing the highest-quality ratings, securities research, monitoring services, operational risk assessments, data, and tools. DBRS Morningstar is a global credit ratings business, formed through the July 2019 acquisition of DBRS by Morningstar, Inc., the ratings business is the fourth-largest provider of credit ratings in the world. DBRS Morningstar is committed to empowering investor success, serving the market through leading-edge technology and raising the bar for the industry. DBRS Morningstar is a market leader in Canada, the U.S. and Europe in multiple asset classes. DBRS Morningstar is driven to bringing more clarity, diversity of opinion, and responsiveness to the ratings process. DBRS Morningstars approach and size provide the agility to respond to customers needs, while being large enough to provide the necessary expertise and resources. The Role: As a Quant Analyst you will execute proprietary research pertaining to building data building various types of credit rating models, such as default models, cashflow models, capital models, regression models covering asset classes of ABS, CMBS, Covered Bond, RMBS, Structured Credit, Corporates, Financial Institutions and Sovereigns. The Credit Ratings Modeling team will collaborate with members from the Credit Ratings, Credit Practices, Independent Review, Data and Technology teams to create class leading models that are as innovative as they are easy to understand in the marketplace. You will be expected to adopt an "iron sharpens iron" attitude where the focus is on making everyone better. The ideal candidate will demonstrate Quant research skills in Credit Modeling alongside Quant Modeling skills such as statistics, Machine Learning, numerical optimization & software engineering skillset within Fintech eco space. This position reports to the Senior Manager of Quantitative Research, Technology. Responsibilities: Support methodology development, Quant Model builds & enhancements for core Quant products as credit predictive models, etc. Participate in building next generation of credit modelling. Maintain and enhance proprietary Python libraries related to model building Leverage structured and unstructured datasets to build new Quant frameworks that would help analysts in informed decision making. Assisting development of Analytics-based solutions, taking ownership of the design and development of solutions to scale information ingestion, storage, computation (training/inference), validation. Participate in analyst conversations for understanding ongoing analyst issues. Requirements: 4 to 5 years of investment research / rating agencies experience with emphasis on fixed income research / analysis, credit modelling. CFA, CQF or postgraduate degree in finance, economics, mathematics, statistics is highly desired. Experience developing Financial Engineering/ Statistical applications on cloud. Experience of statistical models (Regression, Monte Carlo simulations, Numerical Optimization etc.) Experience of developing Quant Models using Python. Experience engineering models on big data. Understanding of both business and technical requirements, and the ability to serve as a conduit between rating team, research and technology Familiarity fixed income. Morningstar is an equal opportunity employer About Us Morningstar DBRS is a leading provider of independent rating services and opinions for corporate and sovereign entities, financial institutions, and project and structured finance instruments globally. Rating more than 4,000 issuers and 60,000 securities, it is one of the top four credit rating agencies in the world. Morningstar DBRS empowers investor success by bringing more transparency and a much-needed diversity of opinion in the credit rating industry. Our approach and size allow us to be nimble enough to respond to customers' needs in their local markets, but large enough to provide the necessary expertise and resources they require. Market innovators choose to work with us because of our agility, tech-forward approach, and exceptional customer service. Morningstar DBRS is the next generation of credit ratings. If you receive and accept an offer from us, we require that personal and any related investments be disclosed confidentiality to our Compliance team (days vary by region). These investments will be reviewed to ensure they meet Code of Ethics requirements. If any conflicts of interest are identified, then you will be required to liquidate those holdings immediately. In addition, dependent on your department and location of work certain employee accounts must be held with an approved broker (for example all, U.S. employee accounts). If this applies and your account(s) are not with an approved broker, you will be required to move your holdings to an approved broker. Morningstars hybrid work environment gives you the opportunity to work remotely and collaborate in-person each week. While some positions are available as fully remote, weve found that were at our best when were purposely together on a regular basis, typically three days each week. A range of other benefits are also available to enhance flexibility as needs change. No matter where you are, youll have tools and resources to engage meaningfully with your global colleagues. R11_DBRSRatingsGmbHIndia DBRS Ratings GmbH, Branch India Legal Entity Show more Show less

Role & Responsibilities Lead implementation of Basel II Advanced Approaches , including internal model validation, IRB documentation, and computation of credit risk parameters such as PD (Probability of Default) , LGD (Loss Given Default) , and EAD (Exposure at Default) . Manage and enhance the Internal Ratings Framework , ensuring effective governance of credit rating models , scorecards, and validation processes. Act as a liaison among business users, technology teams, and third-party vendors to ensure system performance, timely issue resolution, and seamless delivery of enhancements. Conduct end-to-end system testing , including defect validation, data reconciliation, and model logic verification. Lead the development and implementation of the Expected Credit Loss (ECL) framework under Ind AS , including asset staging, lifetime ECL computation, macroeconomic overlays, and forward-looking PD estimates. Drive initiatives under Risk-Based Supervision (RBS) by responding to regulatory queries and closing supervisory gaps. Improve data quality and governance within credit risk systems, ensuring consistency and completeness of critical data elements. Maintain compliance with regulatory guidelines and internal risk policies, including Basel III norms, ICAAP requirements, and RBI circulars. Apply Stress Testing and RAROC (Risk-Adjusted Return on Capital) principles to enhance capital planning and portfolio risk assessment. Work with large datasets for analysis and risk modeling using advanced Excel skills and reporting tools.

You will lead a team of data scientists from top tier schools and collaborate with Founders and business heads to solve complex business problems. Your responsibilities will include developing statistical and machine learning-based models, pipelines, and methods to enhance business processes and engagements. Additionally, you will conduct sophisticated data mining analyses of large data volumes and build data science models for credit and risk underwriting solutions, customer engagement and retention, new business initiatives, and business process improvements. Your role will involve translating data mining results into clear business-focused deliverables for decision-makers. You will work closely with Application Developers to integrate machine learning algorithms and data mining models into operational systems to drive automation, productivity increase, and time savings. Providing technical direction to resolve complex issues and ensure timely delivery of solutions meeting business expectations will also be part of your responsibilities, which may require developing new methods for application. It is essential for you to have a deep understanding of leveraging statistical models in algorithms, experience in multivariate analysis, and identifying parameters affecting customer retention/behavior throughout the customer lifecycle. Proficiency in Python coding and mentoring teams on the same is required. You should possess a strong grasp of the data science landscape and the ability to choose appropriate tools for various problem-solving scenarios. As a structured thinker, you will be expected to bring clarity to any data science problem rapidly. You should be skilled in visualizing data stories, utilizing data visualization tools, and presenting insights as cohesive narratives to senior leadership. Your excellent capability to organize large datasets from diverse sources to derive actionable insights will be crucial. Initiating data science programs within the team and collaborating with other data science teams to establish a knowledge database will also fall under your purview. Skills required for this role include expertise in Data Science, Machine Learning (ML), Python, Statistical Modeling, Credit Modeling, and Fintech.,

Roles and Responsibilities Develop credit risk models using SAS, SQL, and statistical modeling techniques to predict defaults, losses, and other credit-related metrics. Collaborate with cross-functional teams to design and implement effective credit risk strategies that meet business objectives. Conduct stress testing and scenario analysis to identify potential risks and opportunities for growth. Provide data insights and recommendations to stakeholders on loss forecasting, scorecards, and portfolio performance. Desired Candidate Profile 2+ years of experience in Credit Risk Modelling/Analytics or related field. Strong expertise in Basel II/III regulations, CECL/CCAR requirements under IFRS9 framework. Proficiency in programming languages such as Python/R/SAS; strong understanding of machine learning algorithms an added advantage.

Lead Quantitative Analyst, Structured Finance Analytics (Mumbai) The Credit Operations Mumbai Analytics team enables and supports the efficient and effective delivery of credit ratings/information to the market with its specialized skills and assets, consistent frameworks, and economies of scale. We collaborate with stakeholders to build creative, impactful solutions and offer services for the business and the market. About the Role Morningstar DBRS Structured Finance Analytics team is looking for a candidate with a strong problem-solving, analytical, and technical mindset . As a Lead Quant Analyst , you will be responsible for: Managing a team of offshore Quant Analysts Supporting automation of data analysis processes Building and running data analytics to aid rating, research, and surveillance processes Developing and enhancing data analysis, data visualization, and workflow optimization tools We are looking for an individual with a strong understanding of Structured Finance products (RMBS, ABS, CMBS, etc.) and technical skills to build efficient FinTech solutions . Required Technical Expertise: Python, Tableau, AWS, Athena, SQL, and VBA. Location: Navi Mumbai Work Shift: EMEA Responsibilities Manage and mentor a team of 6-8 Quant Analysts , providing guidance on technological enhancement and fostering a. Manage work priorities of the team, including quality & timeliness of delivery schedules , periodic reporting, and regular interactions with the onshore team . Lead and mentor the team on critical processes to achieve business goals. Maintain regular communication with the Global Structured Finance Analytics team and Credit Technology team to ensure projects meet requirements and pass UAT . Lead data transformation, improvement, and integration , based on business requirements. Understand core concepts of data storage and access (SQL, Athena, AWS S3). Implement quick-fix solutions aligned with dynamic business needs . Participate in design and build phases to produce high-quality deliverables . Identify and implement process efficiencies and automation . Collect, organize, and analyze data from internal and external sources for use in criteria development, ratings, and research reports . Take ownership of tasks with a focus on quality and accuracy . Be highly organized with the ability to multi-task and meet tight deadlines . Ensure compliance with regulatory and company policies and procedures . Requirements 9-11 years of relevant experience in Credit Modeling / Model Validation . Qualifications: MBA (Finance) / BTech / PhD (Math) from a Tier I college . Strong analytical skills with experience handling large databases/datasets . Expertise in: Python / Anaconda Data Science Stack (Jupyter, Pandas, NumPy) Tableau, Microsoft Excel, MSSQL Experience with SAS/R is a plus. Familiarity with AWS infrastructure is an added advantage. Ability to manage multiple tasks and deliver results on time . Morningstar DBRS is an equal opportunity employer.