Build a Full ABS Cashflow Waterfall Model (Excel + Python) I am looking for an experienced financial modeler (preferably with ABS/structured finance experience) to build a complete Auto-Loan ABS Cashflow Waterfall Model in both Excel and Python. The model should replicate real-world ABS deal mechanics and should be clean, modular, and easy to audit. Please see requirements below. 1. Collateral Modeling • Build a monthly loan-level or pool-level engine for Auto ABS. • Include: • Scheduled amortization (WAC, WAM) • Prepayments (CPR → SMM logic) • Defaults (CDR → MDR logic) • Loss severity (LGD) • Recovery with lag • Cumulative loss curve generation • Output: • Monthly interest, scheduled principal, prepayments, defaults, recoveries, ending balance. 2. Liability & Tranche Structure Create a dynamic tranche structure including: • Senior, Mezzanine, and Subordinate tranches • User-defined: • Initial principal • Coupon rate • Legal final maturity • Monthly calculations: • Beginning balance • Interest due • Interest paid / shortfalls • Principal paid • Ending balance • WAL, duration, yield ⸻ 3. Waterfall Priority of Payments Full monthly ABS waterfall including: 1. Servicing fees 2. Trustee/admin fees 3. Senior → Mezz → Sub interest payments 4. Senior → Mezz → Sub principal payments (sequential) 5. Excess spread to residual/equity 6. Triggers (basic Overcollateralization / Interest Coverage optional) Waterfall must link seamlessly with collateral and tranche sheets. ⸻ 4. Scenario & Stress Testing • Ability to run Base / Moderate / Severe scenarios. • Adjust CPR, CDR, Severity, and recovery lag. • Output tables + charts for: • Cash flow distribution • Losses by tranche • WAL changes • Yield changes • Break-even loss levels ⸻ 5. Python Version Replicate the entire logic in Python using pandas: • Collateral engine • Tranche engine • Waterfall engine • Scenario functions • Output tables & charts Clean, modular, object-oriented structure preferred. ⸻ 6. Deliverables • Fully functional Excel model with clear formatting and documentation. • Python script/notebook with complete logic and comments. • A short read-me / documentation explaining: • Structure • Assumptions • How to modify the model • Optional (bonus): • Ability to ingest a Bloomberg CFT export (if feasible)