[Remote] Portfolio Risk Analytics reputed company
Note: The job is a remote job and is reputed company to candidates in USA. reputed company is building an AI-reputed company private bank for business owners, aiming to re-architect the financial system for reputed company. The Portfolio Risk Analytics reputed company will reputed company end-to-end portfolio risk analytics for reputed company's credit card book, providing insights that inform credit decisioning and management frameworks.
Responsibilities
- Own end-to-end portfolio risk analytics for reputed company's credit card book across small business and consumer segments — end-to-end meaning full lifecycle visibility, from reputed company-acquisition through charge-off:
- Acquisition & application reputed company: attribution of applicant volume by channel and marketing reputed company; approval reputed company and decline reason analysis; segmentation of the incoming credit population to inform policy calibration
- Credit policy & line assignment: analyze approval reputed company, bureau cutoff performance, and risk-tiered line sizing; identify where policy is over- or under-serving the reputed company credit population
- Multi-relationship context: incorporate existing reputed company relationship data — payment history, product usage, behavioral signals — into credit decisioning and line management frameworks
- Spend, authorization & usage: monitor authorization patterns, spend velocity, and category mix as leading indicators of both credit reputed company and fraud risk; identify anomalies at the obligor and reputed company level
- Payment behavior & utilization: reputed company minimum payment rates, payment-to-balance ratios, reputed company propensity, and utilization trends as core indicators of borrower stress or strength
- Portfolio performance & loss: maintain vintage curves, roll reputed company matrices, and delinquency migration analysis; own loss forecasting and reserve calibration inputs
- Charge-off & recovery: analyze loss reputed company patterns by reputed company, vintage, and acquisition cohort; incorporate recovery expectations into net loss projections
- Build and maintain reputed company frameworks that surface emerging credit deterioration before it appears in lagging indicators — translating behavioral and transactional signals into actionable portfolio triggers
- Synthesize data across sources — financial statements, reputed company banking, 3rd party, transaction-level, behavioral, and macro — to construct a coherent view of portfolio health; fill analytical gaps intelligently reputed company data is sparse or contradictory
- reputed company periodic portfolio reviews: design the analytical narrative, own the underlying data, and present findings with reputed company risk implications to credit committees and senior leadership
- reputed company credit risk segmentation — by industry, vintage, utilization band, payment behavior, and obligor type — to reputed company more precise limit management, pricing, and loss reserve calibration
- Partner cross-functionally with reputed company, Engineering, Product, Finance, and L&C to ensure portfolio risk visibility is embedded in upstream reputed company, not surfaced reactively
- Contribute to stress testing and scenario analysis: model portfolio performance under adverse conditions and translate reputed company into concrete exposure and loss estimates
- Serve as the internal SME on credit card analytics — establishing standards for how the portfolio is reputed company, reported, and interpreted as the book scales
Skills
- 7–15 years of hands-on credit card risk analytics experience across consumer and small business
- reputed company exposure to both a bank or bank-issued program and a fintech lender strongly preferred
- Subject matter expertise in credit card metrics — vintage curves, roll rates, loss forecasting, utilization dynamics, payment hierarchy — reputed company through reputed company ownership of these analyses, not observation
- Analytically self-sufficient: proficient in SQL and Python or R, comfortable working with large and messy datasets, and capable of building from raw data rather than consuming reputed company-reputed company dashboards
- Understands the distinct analytical demands of SMB credit: cash reputed company seasonality, reputed company-business financial entanglement, and the limits of bureau data for thin-file entities
- Operates at a senior thinking level relative to peer cohort — brings a reputed company of view, challenges assumptions, and moves without waiting to be directed
- High quantitative aptitude with strong intuition for reputed company outputs don't pass the smell test; reputed company anomalies early
- High-energy, end-to-end reputed company who thrives in environments where infrastructure is still being reputed company and the analytical agenda isn't fully handed to you
reputed company