Product Risk Manager (Secured)

PeopleStrong; Mumbai; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

PeopleStrong in Mumbai, India, invites an experienced Risk Management professional to lead underwriting strategies for lending products. You will collaborate with Data Science, Product, Operations, and Business teams to align risk policies with growth objectives.

The ideal candidate has 5–8 years in risk management, deep knowledge of MLAP/STSL, and proficiency with R, Python, and SQL to build robust scorecards and models for a high-quality loan portfolio.

Qualifications

• Bachelor's degree in Finance, Business, Economics, or related field; MBA or advanced degree preferred.
• 5-8 years of experience in risk management, underwriting, or portfolio management in financial services.
• Strong understanding of MLAP/STSL policy and risk practices, including fraud detection, credit checks, and process automation.
• Proficiency in risk assessment tools, data analytics, and customer segmentation models.
• Experience in credit risk underwriting, especially for retail or personal loans, is preferred.
• Proficiency in data tools (R, Python, SQL) and a deep understanding of risk models and scorecards.

Responsibilities

• Formulate and execute risk-based underwriting strategies that balance customer acquisition with robust risk management.
• Lead risk segmentation efforts by categorizing loan applicants based on credit scores, financial behaviors, and other key risk indicators to optimize decision-making.
• Develop and refine risk models to improve underwriting decisions and ensure a strong, high-quality loan portfolio.
• Collaborate closely with Data Science, Product, Business, and Operations teams to ensure risk strategies are aligned with broader business goals and objectives.
• Ensure the smooth execution of lending initiatives by integrating risk management processes and protocols throughout operations.
• Evaluate channel partners based on their business models, product offerings, operational processes, and overall creditworthiness, ensuring strategic alignment with organizational risk policies
• Assess and mitigate operational and process risks in collaboration with channel partners, focusing on minimizing external risks to the organization.
• Analyze and understand MLAP/STSL, including fraud mitigation, KYC processes, credit checks, and customer onboarding, ensuring all practices adhere to regulatory standards and mitigate risk.
• Clearly articulate complex risk strategies to both technical and non-technical audiences, including senior management, clients, and channel partners.
• Exceptional presentation skills, capable of representing the organization in discussions with external partners, clients, and stakeholders.
• Demonstrated ability to lead and collaborate with cross-functional teams across data science, product, operations, and business units.
• Strong interpersonal skills to build and maintain effective relationships with internal and external stakeholders.
• Ability to conduct detailed data analysis and risk assessments, interpreting metrics to drive strategic risk management decisions.
• Innovative problem-solving approach to address complex underwriting and risk management challenges.

Skills

Analytical risk management

Lending solutions

Education

Undergraduate / Postgraduate

Tools

R

Python

SQL

Job description

DEPARTMENT-1 Credit,Risk,Legal& Collections

Country India

State Maharashtra

Worksite R-Tech Park Goregaon

Skills

Skill

Analytical Risk Management Lending Solutions

Under Graduate Post Graduate

CERTIFICATION

Job Description

Key Responsibilities

• Formulate and execute risk-based underwriting strategies that balance customer acquisition with robust risk management.
• Lead risk segmentation efforts by categorizing loan applicants based on credit scores, financial behaviors, and other key risk indicators to optimize decision-making.
• Develop and refine risk models to improve underwriting decisions and ensure a strong, high-quality loan portfolio.
• Collaborate closely with Data Science, Product, Business, and Operations teams to ensure risk strategies are aligned with broader business goals and objectives.
• Ensure the smooth execution of lending initiatives by integrating risk management processes and protocols throughout operations.
• Evaluate channel partners based on their business models, product offerings, operational processes, and overall creditworthiness, ensuring strategic alignment with organizational risk policies
• Assess and mitigate operational and process risks in collaboration with channel partners, focusing on minimizing external risks to the organization.
• Analyze and understand MLAP/STSL, including key elements such as fraud mitigation, KYC processes, credit checks, and customer onboarding, ensuring all practices adhere to regulatory standards and mitigate risk.
• Clearly articulate complex risk strategies to both technical and non-technical audiences, including senior management, clients, and channel partners.
• Exceptional presentation skills, capable of representing the organization in discussions with external partners, clients, and stakeholders.
• Demonstrated ability to lead and collaborate with cross-functional teams across data science, product, operations, and business units.
• Strong interpersonal skills to build and maintain effective relationships with internal and external stakeholders.
• Ability to conduct detailed data analysis and risk assessments, interpreting metrics to drive strategic risk management decisions.
• Innovative problem-solving approach to address complex underwriting and risk management challenges.

Qualifications

• Bachelor's degree in Finance, Business, Economics, or a related field (MBA or advanced degree preferred).
• 5-8 years of experience in risk management, underwriting, or portfolio management in the financial services industry.
• Strong understanding of MLAP/STSL policy and the associated risk management practices, including fraud detection, credit checks, and process automation.
• Proficiency in risk assessment tools, data analytics, and customer segmentation models.
• Experience in credit risk underwriting, especially for retail or personal loans, is preferred.
• Proficiency in data tools (R, Python, SQL) and a deep understanding of risk models and scorecards.