Slope is looking for a Data Scientist to build the data science foundation that transforms noisy transactional data into actionable signals for credit decisioning, aiming to automate underwriting and enable instant credit access for small businesses.
Requirements
- Hands-on experience with sales/transaction data (e.g., card processing, e-commerce, platform transaction data).
- Strong modeling and statistical chops: ideally has developed & shipped fraud or credit risk models E2E.
- Comfortable writing production-quality code (Python, SQL, ideally some familiarity with data pipelines).
- Experience in credit risk, merchant lending, or BNPL.
- Familiarity with data architecture for high-volume transactional platforms.
Responsibilities
- Build and productionize models that map sales and payment data into reliable credit signals and pre-approval pipelines.
- Develop frameworks to handle fragmented, messy sales data and normalize it for underwriting use.
- Partner closely with Product, Engineering, and Risk to design pre-approval strategies and continuously improve model accuracy
- Conduct exploratory analyses to identify leading indicators of merchant growth, default risk, and repayment capacity.
- Translate insights into tools and dashboards that underwriters and product teams can act on.
- Establish best practices for experimentation, back-testing, and data validation in underwriting contexts.
Other
- 3–6+ years experience as a Data Scientist, ideally in fintech, lending, or capital products.
- Background in underwriting, risk modeling, or capital-as-a-service products (Stripe Capital, Shopify Capital, Square Loans, etc. experience highly valued).
- A bias for action—you enjoy building MVPs and iterating quickly with stakeholders.
- Excellent communication skills: able to translate technical insights into business outcomes.
- Prior startup experience (0→1 or scaling phase).
