Credit Risk Modeler

Ford Motor Company; Chennai District; Presencial; INR 900 000 - 1 500 000; Tempo integral

Resumo da oferta

Ford Motor Company in Chennai seeks an analytics professional to drive credit risk analytics, model development, validation, and reporting. You will translate business problems into quantitative models and deliver insights to stakeholders using SQL, SAS, Python, Excel and PowerPoint.

Responsibilities include IFRS 9 modeling (PD/LGD/EAD), independent validation, dashboarding, and management reporting. 3–5 years in Banking/Financial Services is preferred; cloud analytics experience is a plus.

Qualificações

• Masters in a quantitative field or related discipline.
• Strong knowledge of PD, LGD, EAD modeling and IFRS 9.
• Experience with statistical methods and ML techniques.
• Proficiency in regression, clustering, CHAID/CART, ANOVA/ ANCOVA.
• Experience with cloud-based analytics in a GC P or similar setup.
• Excellent communication and presentation skills.

Responsabilidades

• Support credit risk analytics across model development, validation, monitoring and reporting.
• Data extraction, reconciliation, quality checks using SQL, SAS, Python, Excel and PowerPoint.
• Translate findings into insights and communicate with stakeholders across teams.
• Maintain documentation of analysis, validation and reporting logic.
• Assist IFRS 9 model development including PD, LGD, EAD and loss estimation.
• Perform independent validation of model methodology, data and outputs.
• Analyze portfolio trends and risk drivers to support decision making.
• Produce recurring reports, dashboards, and management packs on schedule.

Conhecimentos

PD/LGD/EAD modeling

Predictive modeling

Statistics & ML techniques

Regression/ANOVA/CHAID/CLUSTER

Team collaboration

Cloud analytics (GCP)

Communication skills

Formação académica

Masters in Finance/Analytics/Math/CS/Statistics/OR

Masters in Math/Stats/Engineering or related

Ferramentas

R

SAS

Python

Excel

PowerPoint

Word

Descrição da oferta de emprego

Job 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

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.

Qualifications

• Masters in Mathematics/Statistics/Economics/Engineering or any other related discipline or a track record of performance that demonstrate this ability

• Good exposure to ML techniques such as Clustering/classification/decision trees, Random forests and related algorithms

• Excellent problem solving, communication, and data presentation skills

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

Requirements: