Credit Risk Modeler

Ford Motor Company; Chennai; On-site; INR 900,000 - 1,800,000; Full time

Job summary

Ford in Chennai, India seeks an analytics professional to translate business problems into quantitative models for credit risk. You will develop, validate, and monitor IFRS 9 related models, using SQL, SAS, Python, and ML techniques, and communicate insights to stakeholders.

Candidates should hold a Masters in a quantitative field and have 3–5 years in banking/financial services, with strong data presentation, documentation, and collaboration skills. Experience in cloud analytics is a plus.

Qualifications

• Masters in Mathematics/Statistics/Economics/Engineering or related discipline.
• Exposure to ML techniques such as clustering/classification/decision trees, random forests.
• Excellent problem solving, communication, and data presentation skills.

Responsibilities

• Support credit risk analytics across model development, validation, monitoring, and reporting.
• Data extraction, reconciliation, QA using SQL, SAS, Python, Excel, and PowerPoint.
• Translate analytical findings into insights for stakeholders and model teams.
• Document analysis, validation observations, reporting logic and findings.
• Assist IFRS 9 model development (PD, LGD, EAD, ECL) and risk frameworks.
• Conduct independent validation of methodologies and inputs.
• Analyze portfolio trends and risk drivers to support decisions.
• Produce recurring reports, dashboards, and MI packs on time.

Skills

PD/LGD/EAD modeling

Predictive modeling

Statistics & ML

Regression/Classification/Clustering

R, SAS, Python

Excel/PowerPoint/Word

Communication skills

Cloud analytics (GCP)

Education

Masters in Finance

Masters in Financial Engineering

Masters in Analytics

Masters in Mathematics

Masters in Computer Science

Masters in Statistics

Masters in Industrial Engineering

Operations Research

Related field

Tools

SQL

SAS

Python

Excel

PowerPoint

Word

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.

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.

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