(Senior) - Credit Risk Analyst

The Credit Risk Management team owns risk policy, analytics, and operations for MoMos PayLater (Ví Trả Sau) product. The teams scope runs from underwriting and limit management through scoring, fraud and merchant risk control, portfolio monitoring and operations, recovery, and risk reporting to leadership, finance, and lending partners. Mô tả công việcDesign and tune underwriting policy - approval rules, score cutoffs, offer structures, and eligibility segments - measured with champion/challenger setupsRun credit-limit management: eligibility criteria, limit increase/decrease decisions, impact sizing, execution, and post-change monitoringDevelop, validate, and monitor risk scores and models; run their production scoring pipelines and manage migrations into decisioningRun recovery analytics and operations: channel experiments, vendor performance evaluation, and allocation processesDetect and block cash-out and abuse patterns; maintain merchant- and transaction-level risk controls; assess risk and set launch controls for new products, merchant channels, and payment use casesExecute account-level risk actions on the live portfolio, with verification and post-action monitoringMonitor portfolio performance, forecast losses, and investigate portfolio movementsVerify decisioning changes before and after each release; monitor funnel health; investigate production issues in decisioning and data through to root cause with engineering teamsBuild and maintain the data pipelines, scheduled jobs, monitoring, and applied AI/automation tooling that risk policies and reporting run onProduce analyses, decision memos, and recurring risk reporting for leadership, finance, and lending partners; support partner data requests and reconciliations; adapt policy and data handling to regulatory and data-privacy requirements; take end-to-end ownership (PIC) of production decision processesYêu cầu công việcBachelor's degree in a quantitative or engineering fieldHands-on experience in data analyticsStrong SQL on large transactional datasetsSolid statistical grounding for analysis and experimentationDiscipline in verifying results against source dataClear written communication in Vietnamese and EnglishPreferred:Python for analysisExperience in consumer lending, credit risk, or paymentsExperiment design in practice: holdouts, A/B tests, difference-in-differencesProduction tooling: Git, Airflow, BigQuery; Spark/Iceberg lakehouse experienceFluency with AI-assisted workflows, paired with the ability to critically validate AI-generated code and analyses against ground truth