DVP - Consumer Risk

About Credit Saison India

Established in 2019, Credit Saison India (CS India) is one of the countrys fastest growing Non-Bank Financial Company (NBFC) lenders, with verticals in wholesale, direct lending and tech-enabled partnerships with Non-Bank Financial Companies (NBFCs) and fintechs. Its tech-enabled model coupled with underwriting capability facilitates lending at scale, meeting Indias huge gap for credit, especially with underserved and under penetrated segments of the population.

Credit Saison India is committed to growing as a lender and evolving its offerings in India for the long-term for MSMEs, households, individuals and more. Credit Saison India is registered with the Reserve Bank of India (RBI) and has an AAA rating from CRISIL (a subsidiary of S&P Global) and CARE Ratings.

Currently, Credit Saison India has a branch network of 80+ physical offices, 2.06 million active loans, an AUM of over US$2B and an employee base of about 1,400 employees.

Credit Saison India is part of Saison International, a global financial company with a mission to bring people, partners and technology together, creating resilient and innovative financial solutions for positive impact. Across its business arms of lending and corporate venture capital, Saison International is committed to being a transformative partner in creating opportunities and enabling the dreams of people.

Saison International is the international headquarters (IHQ) of Credit Saison Company Limited, founded in 1951 and one of Japans largest lending conglomerates with over 70 years of history and listed on the Tokyo Stock Exchange. The Company has evolved from a credit-card issuer to a diversified financial services provider across payments, leasing, finance, real estate and entertainment. Based in Singapore, Saison Internationals global operations span over Singapore, India, Indonesia, Thailand, Vietnam, Mexico, Brazil, with active investments in debt, equity, corporate venture capital, and technology.

Roles & Responsibilities:

• Conduct Portfolio Analysis and Monitor Portfolio delinquencies at a micro level, identification of segments, programs, locations, and profiles which are delinquent or working well.
• Design and Implement risk strategies across the customer lifecycle (acquisitions, portfolio management, fraud, collections, etc.)
• Identify trends by performing necessary analytics at various cuts for the Portfolio
• Provide analytical support to various internal reviews of the portfolio and help identify the opportunity to further increase the quality of the portfolio
• Use data-driven insights to improve risk selection, pricing, and capital allocation.
• Work with Product team and engineering team to help implements the Risk strategies
• Work with Data science team to effectively provide inputs on the key model variables and optimize the cut off for various risk models
• Create a deep level understanding of the various data sources (Traditional as well as alternate) and optimum use of the same in underwriting
• Should have good understanding about various unsecured credit products
• Should be able to understand the business problems and helps solving them by the analytical methods
• Build and lead high performing credit risk team
• Identify emerging risks, concentration issues, and early warning signals
• Enhance automation, digital underwriting, and advanced analytics in credit risk processes.
• Improve turnaround times, data quality, and operational efficiency without compromising risk standards

Required skills & Qualifications:

• Strong expertise in credit risk management, underwriting strategies, and portfolio analytics
• Excellent stakeholder management and communication abilities
• Strategic mindset with the ability to balance risk and growth
• Advanced analytical and problem-solving skills
• Bachelor's degree in Computer Science, Engineering or related field from top tier (IIT/IIIT/NIT/BITS)
• 9+ years of experience working in Data science/Risk Analytics/Risk Management with experience in building the models/Risk strategies or generating risk insights
• Proficiency in SQL and other analytical tools/scripting languages such as Python or R
• Deep understanding of statistical concepts including descriptive analysis, experimental design and measurement, Bayesian statistics, confidence intervals, Probability distributions
• Proficiency with statistical and data mining techniques
• Proficiency with machine learning techniques such as decision tree learning etc.
• Should have an experience working with both structured and unstructured data
• Fintech or Retail consumer digital lending experience is preferred