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Open nowPosted today

Credit Risk Modeler

Ford Global911 open roles

Where
Chennai, Tamil Nadu, India
Work mode
On site
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Your applicationOpen nowCredit Risk ModelerFord Global · Chennai, Tamil Nadu, India

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  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

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The clock on this job

Early applications get read.

13.7% of postings close within 7 days. Measured by our own scanner across the market. Ford Global postings stay open a median of 13 days.

Share of postings closed within
  1. 2.3%1 day
  2. 4.8%3 days
  3. 13.7%7 days
  4. 22.1%14 days
  5. 37.6%30 days
  6. 59.1%60 days
This job: posted today

Ford Global median: 13 days open

The posting

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
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