Cofounder - Head of Risk & Actuarial

Every year, over 700,000 workers, students, and apprentices from low-income countries arrive in the EU needing €2,500–€12,000 upfront to cover visa fees, language tests, qualification recognition, and relocation. No one finances this. Banks won't touch it. Families go into debt.

Workabroad Advance is our answer: an income-share agreement product that finances migration costs and collects repayments via employer-mediated payroll, once the worker is placed and earning. The SAM is €6.4B/year in the EU alone. Nobody has built this. You will.

What you'll do

• Build the ISA underwriting model — default probability by borrower profile, income trajectory assumptions, and loss-given-default curves — without credit bureau data
• Set income-share rates and repayment caps by borrower cohort
• Stress-test the portfolio against macroeconomic shocks, default clustering, and income interruption events
• Structure the warehouse debt facility — tranche sizing, loss triggers, investor reporting
• Define the path to ABS securitisation at scale
• Lead regulatory classification of the product under BaFin and AFM consumer credit frameworks
• Co-own the company as a founding team member

What you bring

• Part-qualified or fully qualified actuary (SOA, IFoA, or DAV) — or equivalent depth in structured credit, consumer lending, or quantitative risk
• Hands-on experience building credit scorecards, PD/LGD models, or pricing models — not just studied them
• Comfort underwriting thin-file or no-file borrowers — emerging-market or migrant lending experience is a strong differentiator
• Working knowledge of structured finance mechanics: waterfalls, tranching, loss absorption, warehouse facilities
• Founder mindset — you want to build, not maintain

• Greenfield category — not many comparable ISA products for this market in Europe
• Cofounder equity + reasonable comp
• Structural collection advantage that no standalone lender can replicate
• Direct path to ABS and institutional debt capital at scale

How would you build a default probability model for a worker from the Philippines entering the German labour market, with no German credit history? Walk us through the variables, data sources, and assumptions.