Machine Learning Engineer (Core ML)

#SWE-AVN

Our client, a pioneer in reshaping consumer credit using innovative technology, is committed to reducing capital costs. They strongly believe in universal access to capital, crucial in today's capital-based economy, with over $1 trillion in unsecured credit card revolving balances.

The journey of credit cards since the '60s, culminating in balances exceeding $1 trillion in 50 years, reveals the existing challenges. Unsecured credit, despite its efficiency in quick underwriting and access to capital in 1-3 days, comes at a high cost (15-25% APR). Conversely, secured capital involves high origination costs and prolonged processes (weeks in underwriting, months for access) but offers cheaper rates (<10% APR).

Innovating the credit landscape, our client introduced a new asset-backed credit card, securing equity in homes, cars, and other assets. This innovation allows them to offer remarkably low APRs to consumers. Their flagship product boasts the world's lowest APR credit card, aiming to change the credit paradigm.

Funding:

Backed by substantial equity funding of over $125 million, our client comprises a well-capitalized team comprising technology (Microsoft, Facebook) and finance (Square, Discover, CapitalOne, Goldman) experts. With top-tier investors like Founders Fund, Khosla Ventures, Caffeinated Capital, Max Levchin, NYCA, and Sequoia, our client continues to lead the charge in transforming consumer credit. Additionally, they recently secured Series C funding, further solidifying their position in this innovative endeavor.

Role:
At our client's company, the primary focus lies in crafting intricate financial workflows, especially regarding the facilitation of credit lines and the retrieval of payments from individuals.

Key Areas of Emphasis for ML Engineers at Our Client's Company:

1. Response Modeling

2. Risk Modeling

3. Building models to estimate income and employment stability

4. Designing Pricing Models

5. Creating Anti-Fraud Models

6. Automating human operations for increased efficiency