# Portfolio Risk Analytics Lead

**Company:** [Flex](https://hotfix.jobs/companies/flex)
**Location:** Remote
**Role:** Data Analytics
**Salary:** $150k – $250k/yr
**Experience:** 7+ years
**Skills:** SQL, Python, R, Credit Risk Analytics, Vintage Curves, Roll Rates, Loss Forecasting, Portfolio Segmentation, Stress Testing, pandas, Data Analysis, Risk Modeling
**Posted:** 2026-05-09

> Leads end-to-end portfolio risk analytics for Flex's credit card book, covering acquisition to charge-off, building early warning systems, and conducting stress testing. Requires 7-15 years in credit card risk analytics, SQL/Python proficiency, and expertise in SMB credit dynamics.

## Job Description

## Core Responsibilities

- Own end-to-end portfolio risk analytics for Flex's credit card book across small business and consumer segments — 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

- 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

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