Job Title: Credit Risk Model Developer / Risk Analytics Lead
Experience Required: 9 years

Skills & Expertise:

• LGD (Loss Given Default)

• EDA (Exploratory Data Analysis)

• Model Development (Credit Risk Scoring Models)

• Credit Risk Management

• Team Management

• Banking/Financial Sector Background

• Quantitative Risk Management

Key Responsibilities:

Develop Credit Risk Scoring Models:

• Solve analytically complex problems related to credit risk, focusing on model development for clients in the financial sector.

Lead Modeling Workstream:

• Lead the development of credit rating models, which involves defining data requirements, data cleaning, aggregation, statistical analysis, and coaching junior team members through the modeling process.

Provide Expert Guidance:

• Advise the consulting team on modeling-related issues, ensuring best practices are followed.

Support Risk Management Tools:

• Contribute to the development and maintenance of proprietary risk management tools and knowledge development projects.

Employee Specifications:

Educational Background:

• A postgraduate degree (preferably in Statistics, MBA, or Economics with a quantitative focus) from a reputed institution (e.g., DSE, ISI, JNU, IIMs, MDI, etc.) with a strong academic record.

Experience:

• 1-9 years in a reputable bank, insurance company, financial firm, or analytics firm, with at least 2 years of experience in risk management.

• Specific exposure to credit risk or market risk is essential.

• Experience in quantitative risk management is required.

• Experience with scorecard development and risk rating development.

Skills:

• Strong understanding of both business and the quantitative foundations of risk management.

• Excellent problem-solving skills, including the ability to break down issues, identify root causes, and propose solutions.

• Strong communication skills, both verbal and written, with fluency in English.

• Ability to work under pressure and meet tight deadlines in a dynamic, evolving environment.

• Strong team collaboration skills and coaching abilities.

Technical Expertise:

• Quantitative Techniques: Multivariate Statistics, Econometrics, Decision Trees (CART, CHAID), Optimization, Machine Learning, Stochastic Processes, etc.

• Familiarity with tools such as SAS, SPSS, Answer Tree, Crystal Ball, @Risk, SAS Eminer, Knowledge Studio, Xeno, and Model Builder is a plus.