Manager, Credit Risk Modelling at Visa

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

19 May, 26

Salary

0.0

Posted On

18 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Credit Risk Modeling, Model Development, Model Validation, Stakeholder Engagement, Feature Engineering, Probability of Default, Loss Given Default, Exposure at Default, IFRS 9/ECL, Stress Testing, Credit Scoring Models, Alternate Data Sources, SAS, Python, SQL, Machine Learning

Industry

IT Services and IT Consulting

Description

Company Description Visa is a world leader in payments technology, facilitating transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories, dedicated to uplifting everyone, everywhere by being the best way to pay and be paid. At Visa, you'll have the opportunity to create impact at scale — tackling meaningful challenges, growing your skills and seeing your contributions impact lives around the world. Join Visa and do work that matters — to you, to your community, and to the world. Progress starts with you. Job Description 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 Domain-driven feature engineering, with proven ability to transform high-volume raw data into risk sensitive variables that optimizes predictive power of the credit-risk scorecards, models. Worked in end to end model development projects — from requirements gathering to implementation — across Probability of Default (PD), Loss Given Default (LGD), Exposure at Default (EAD), stage allocation according to SICR, IFRS 9/ECL, and stress testing frameworks Design and recalibration of 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. 6+ years of experience in consumer or commercial credit risk modeling 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 using both traditional and advanced neural networks/Gradient Boosted Trees. 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, 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 ensuring that all models remain fully explainable Experience working across different geographies and regulatory regimes Additional Information Visa is an EEO Employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with EEOC guidelines and applicable local law. Job Family Group: Product Development

Responsibilities

The role involves championing project delivery and leading the design, development, validation, and implementation of advanced credit risk models for diverse clients across various lending sectors. Key duties include collaborating with internal teams to understand business needs and engaging with clients to address challenges and regulatory contexts.