The Portfolio Risk Analytics Lead oversees credit risk analytics for small business and consumer credit portfolios, focusing on risk assessment, analysis, and stakeholder reporting.
Flex is building the AI-native private bank for business owners.
We’re re-architecting the entire financial system for entrepreneurs—from the first dollar a business earns to how that value compounds, moves, and is ultimately spent in real life. Banking, credit, payments, personal finance, and financial operations—rebuilt from the ground up as a single, intelligent system. Flex is the full financial home for ambitious owners.
Since launching publicly in September 2023, Flex has scaled from zero to nine-figure annualized revenue, with a clear path to profitability by late 2026. We move fast, ship relentlessly, and operate with extreme ownership.
Our customers are affluent business owners ($3–$200M in revenue)—the backbone of the economy and one of the most underserved segments in finance. They’re stuck with outdated banks and fragmented tools. We’re replacing all of it. The opportunity is massive: a ~$1T+ revenue market hiding in plain sight. Our ambition is to build a product that is fundamentally better—not incrementally improved.
Flex Fuels Ambition.
The Portfolio Risk Analytics Lead is a key member of the Flex Risk Management Leadership Team (reports to the Chief Risk Officer) who will have the opportunity to take the intelligence engine at Flex to a level that rivals the best in class.
Core Responsibilities
- Own end-to-end portfolio risk analytics for Flex's credit card book across small business and consumer segments — end-to-end meaning full lifecycle visibility, from pre-acquisition through charge-off:
- Acquisition & application flow: attribution of applicant volume by channel and marketing source; approval rate and decline reason analysis; segmentation of the incoming credit population to inform policy calibration
- Credit policy & line assignment: analyze approval thresholds, bureau cutoff performance, and risk-tiered line sizing; identify where policy is over- or under-serving the target credit population
- Multi-relationship context: incorporate existing Flex 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 quality and fraud risk; identify anomalies at the obligor and segment level
- Payment behavior & utilization: track minimum payment rates, payment-to-balance ratios, revolve propensity, and utilization trends as core indicators of borrower stress or strength
- Portfolio performance & loss: maintain vintage curves, roll rate matrices, and delinquency migration analysis; own loss forecasting and reserve calibration inputs
- Charge-off & recovery: analyze loss emergence patterns by segment, vintage, and acquisition cohort; incorporate recovery expectations into net loss projections
- Build and maintain early warning 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, open banking, 3rd party, transaction-level, behavioral, and macro — to construct a coherent view of portfolio health; fill analytical gaps intelligently when data is sparse or contradictory
- Lead periodic portfolio reviews: design the analytical narrative, own the underlying data, and present findings with clear risk implications to credit committees and senior leadership
- Develop credit risk segmentation — by industry, vintage, utilization band, payment behavior, and obligor type — to enable more precise limit management, pricing, and loss reserve calibration
- Partner cross-functionally with Underwriting, Engineering, Product, Finance, and L&C to ensure portfolio risk visibility is embedded in upstream decisions, not surfaced reactively
- Contribute to stress testing and scenario analysis: model portfolio performance under adverse conditions and translate output into concrete exposure and loss estimates
- Serve as the internal SME on credit card analytics — establishing standards for how the portfolio is measured, reported, and interpreted as the book scales
Qualifications
- This role sits in the foundational build path of core risk management disciplines, and we expect significant upward potential for the right candidate. The emphasis is on finding colleagues with a strong foundation more than a ‘minimum number of years’ constraint. We can work with folks who have 7–15 years of hands-on credit card risk analytics experience across consumer and small business; direct 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 — built through direct 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 pre-built dashboards
- Understands the distinct analytical demands of SMB credit: cash flow seasonality, owner-business financial entanglement, and the limits of bureau data for thin-file entities
Mindset
- Operates at a senior thinking level relative to peer cohort — brings a point of view, challenges assumptions, and moves without waiting to be directed
- High quantitative aptitude with strong intuition for when outputs don't pass the smell test; catches anomalies early
- High-energy, end-to-end owner who thrives in environments where infrastructure is still being built and the analytical agenda isn't fully handed to you
Similar Jobs
Artificial Intelligence • Cloud • Enterprise Web • Information Technology • Software • Analytics • Business Intelligence
The Enterprise Account Executive drives sales in a new market, develops strategic account plans, engages with C-Suite executives, and maintains client relationships, ensuring customer needs are met while exceeding sales quotas.
Top Skills:
Crm SoftwareMicrosoft Office SuiteSales ToolsSalesforce
Cloud • Information Technology • Security • Software • Cybersecurity
The Senior Product Manager will lead data classification capabilities in a Data Security platform, overseeing product strategy and collaborating with various teams to drive accuracy and compliance.
Top Skills:
AIData ClassificationData PrivacyData SecurityLarge Language ModelsMachine LearningNlp
Beauty • Robotics • Design • Appliances • Manufacturing
The Senior Consumer Insights Analyst leads consumer insights within cross-functional teams by managing research processes, synthesizing data, and delivering actionable findings to influence product development and strategy.
Top Skills:
Consumer ResearchData Analysis
What you need to know about the Austin Tech Scene
Austin has a diverse and thriving tech ecosystem thanks to home-grown companies like Dell and major campuses for IBM, AMD and Apple. The state’s flagship university, the University of Texas at Austin, is known for its engineering school, and the city is known for its annual South by Southwest tech and media conference. Austin’s tech scene spans many verticals, but it’s particularly known for hardware, including semiconductors, as well as AI, biotechnology and cloud computing. And its food and music scene, low taxes and favorable climate has made the city a destination for tech workers from across the country.
Key Facts About Austin Tech
- Number of Tech Workers: 180,500; 13.7% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Dell, IBM, AMD, Apple, Alphabet
- Key Industries: Artificial intelligence, hardware, cloud computing, software, healthtech
- Funding Landscape: $4.5 billion in VC funding in 2024 (Pitchbook)
- Notable Investors: Live Oak Ventures, Austin Ventures, Hinge Capital, Gigafund, KdT Ventures, Next Coast Ventures, Silverton Partners
- Research Centers and Universities: University of Texas, Southwestern University, Texas State University, Center for Complex Quantum Systems, Oden Institute for Computational Engineering and Sciences, Texas Advanced Computing Center
