Senior Manager

Job Description

Role overview

The individual will be a member of VCA Intelligence & Data Solutions team in Asia Pacific. The position will be based in Visa’s Bangalore office.

As Credit Risk Modeling Lead, you will champion project delivery and lead design, development, validation, and implementation of advanced credit risk models for a diverse portfolio of clients across banking, fintech, and other lending sectors.

You will combine deep technical and domain expertise with strong stakeholder engagement skills to deliver high‑impact solutions that meet regulatory requirements, enhance credit decision‑making, and drive business performance.

This role offers the opportunity to work on multiple projects, across regions, and with varied data environments — making adaptability and client‑centric thinking essential.

Key responsibilities

• Collaborate with data science leads and consulting leads to understand business needs and identify strategic areas where data science/modeling can drive strong business impact, in alignment with organization's goals and priorities

• Engage with client stakeholders to understand business challenges, risk appetite, and regulatory context

• Lead end to end model development projects — from requirements gathering to implementation — across Probability of Default (PD), Loss Given Default (LGD), Exposure at Default (EAD), IFRS 9/ECL, and stress testing frameworks

• Design and implement credit scoring models leveraging alternate data sources (e.g., telco usage, e‑commerce behavior, social and utility payment data, open banking, and digital footprint) to expand credit access and improve risk assessment

• Present complex analytical findings in clear, actionable terms for senior executives and risk committees, provide best practice guidance on risk model governance, regulatory compliance, and validation standards

• Support data science leads in identifying opportunities for cross selling data analytics, AI/ML, and adjacent risk modeling services

• Support proposal writing, RFP responses, and client presentations to win new engagements

• Guide junior data scientists and analysts in technical modeling techniques and client delivery best practices, ensure quality and timeliness of project delivery

• Promote knowledge sharing across project teams and practices

This is a hybrid position. Expectation of days in office will be confirmed by your hiring manager.

Qualifications

Basic Qualifications

• Bachelor’s or Master’s degree in Statistics, Mathematics, Economics, Finance, Data Science, or a related quantitative discipline, PhD preferred for advanced modeling leadership roles.
• 12+ years of experience in credit risk modeling, with at least 3-4 years in a team lead or project management role
• •Proven track record in developing, validating, and implementing PD, LGD, EAD, IFRS 9/ECL, and stress testing models
• Experience across the full credit lifecycle — application, behavioral, collections, and portfolio monitoring models
• Demonstrated expertise in alternate data credit scoring e.g., telco, utility, social, e commerce, open banking data and integrating it into traditional risk frameworks
• Advanced proficiency in statistical programming languages such as SAS, R, Python, and SQL
• Experience with machine learning techniques e.g., gradient boosting, random forests, neural networks and ensuring model explainability and fairness
• Ability to lead cross functional modeling teams and manage multiple projects simultaneously
• Strong stakeholder management skills — able to engage with internal data teams, risk committees, regulators, auditors, and client leaders
• Excellent communication skills — able to translate complex analytics into actionable business insights.
• Continuous learning mindset — staying updated on emerging modeling techniques, regulatory changes, and industry best practices

Preferred Qualifications

• Experience in a top tier consulting environment or risk advisory role
• Exposure to machine learning methods e.g., XGBoost, LightGBM, neural networks with an understanding of explainability requirements
• Experience working across different geographies and regulatory regimes