Description

Potential candidates should have knowledge in general math modeling, Machine Learning (ML) and related Operations Research (OR) and simulation techniques. Candidates should have hands-on experience in developing quantitative models. They should possess the ability to translate a business problem into an analytical problem, recommend, implement and validate quantitative models, and generate/deliver insights to stakeholders. Preference would be given to candidates that are intellectually curious, innovative thinkers, and have demonstrated ability to solve problems independently.

Responsibilities

Roles & Responsibilities

- Support credit risk analytics activities across model development, independent validation, monitoring, and business reporting.

- Conduct data extraction, reconciliation, quality checks, and analytical review using tools such as SQL, SAS, Python, Excel, and PowerPoint.

- Translate analytical findings into clear insights and communicate results to model development, validation, business, and risk stakeholders.

- Maintain clear documentation of analysis, validation observations, reporting logic, assumptions, and key findings.

- Assist in the development and enhancement of IFRS 9 models, including PD, LGD, EAD, expected credit loss, and related risk estimation frameworks.

- Perform independent validation activities by reviewing model methodology, assumptions, input data, outputs, performance, and documentation.

- Analyze portfolio trends, credit risk drivers, delinquency patterns, default behavior, and model performance indicators to support risk decision-making.

- Execute recurring business reports, dashboards,



and management information packs accurately and within agreed timelines.

Qualifications

Qualifications

- Masters in Mathematics/Statistics/Economics/Engineering or any other related discipline or a track record of performance that demonstrate this ability
- Positive exposure to ML techniques such as Clustering/classification/decision trees, Random forests and related algorithms
- Excellent problem solving, communication, and data presentation skills

Skills/Knowledge required

- Masters in Finance, Financial Engineering, Analytics or Mathematics, Computer Science, Statistics, Industrial Engineering, Operations research, or related field.

- Good understanding of Probability of Default (PD), LGD and EAD modeling technique.

- Very good understanding of Predictive modeling techniques and their application.

- Statistics and machine learning techniques.

- Conducted and applied statistical methodologies including linear regression, logistic regression, ANOVA/ANCOVA, CHAID/CART, cluster analysis

- Team player and collaboration skills.

- Programming skills in R, SAS, and PYTHON.

- Fluency with Excel, PowerPoint and Word

- Strong written and oral presentation / communication skills – must have the ability to convey complex information simply and clearly

- Experience with developing and implementing cloud based analytical solutions in GCP or similar set up.

Experience (Good to have):

- 3 - 5 Years exposure in Banking & Financial Services industry

- Candidate should have worked in Credit Analytics (Mandatory) and preferably in Financial Analytics, Retail bank, Mortgage, Lending / liability product

- Risk Analytics, Credit Risk Scorecard Development, Model Validation, IFRS 9 Validations, Credit Loss Forecasting

📌 Credit Risk Modeler (Chennai)
🏢 Ford Motor
📍 Chennai