Overview
This role is part of a Risk & Analytics team responsible for guiding business strategy through data-driven decision-making. The team designs experiments, analyzes performance, and builds tools and frameworks to optimize resource allocation and support sustainable growth. The Credit team collaborates cross-functionally with Machine Learning, Product, Engineering, Capital Markets, and Commercial teams to manage the risk profile responsibly, focusing on credit policies, testing, and adapting to market changes.
Responsibilities
- Use advanced data analytics to generate insights and optimize credit strategies across products and regions.
- Collaborate with Engineering to design and implement scalable risk models and credit risk capabilities.
- Monitor portfolio performance and macroeconomic trends affecting loan outcomes; adjust underwriting and marketing strategies proactively to mitigate risk.
- Work closely with Product, Legal, and Compliance teams to interpret regulatory and market requirements across jurisdictions and translate them into credit policy, underwriting, and product design recommendations.
- Coordinate with external stakeholders such as merchants, vendors, and regulatory bodies to align credit risk practices, ensure compliance, and strengthen partnerships.
- Oversee the development and execution of credit underwriting frameworks balancing growth, compliance, and risk mitigation.
- Lead cross-functional discussions to ensure new product launches and market entries align with risk appetite, operational capabilities, and local regulations.
Requirements
- Degree in Data Science, Computer Science, Engineering, Economics, or a related field.
- 4+ years of experience or equivalent senior-level experience.
- Experience setting credit strategy for new products using sandbox data, retrospective studies, or archives.
- Experience in high-line unsecured lending, particularly in the home improvement vertical.
- Proficiency in SQL, Python, or other scripting languages.
- Skills in data mining, data visualization, and statistical modeling.
- Experience applying machine learning techniques to credit risk management.
- Ability to leverage advanced analytics to develop and optimize credit strategies.
- Experience monitoring and interpreting model performance metrics across portfolios.
- Proven leadership in cross-functional initiatives bridging Product, Legal, Compliance, and Engineering to align credit strategies with regulatory frameworks and business goals.
- Deep understanding of consumer lending regulations, fair lending principles, and regional market dynamics affecting credit policy and underwriting.
- Ability to translate complex regulatory and economic insights into actionable credit and product strategies.
- Demonstrated success mentoring analytical teams and driving data-informed decision-making at scale.
- Exceptional communication skills with the ability to influence senior stakeholders across technical and non-technical functions.
Compensation & Benefits
- Pay Grade: M
- Equity Grade: 8
- Base pay range for USA (CA, WA, NY, NJ, CT): $180,000 - $230,000 per year
- Base pay range for USA (all other states): $160,000 - $180,000 per year
- Total compensation may include equity rewards, monthly stipends for health, wellness, and technology spending, and benefits.
- Benefits include 100% subsidized medical coverage, dental and vision for employees and dependents.
- Flexible spending wallets for technology, food, lifestyle needs, and family forming expenses.
- Competitive vacation and holiday schedules.
- Employee stock purchase plan allowing discounted share purchases.
Location
- Remote-first position with flexibility to work from almost anywhere within the country of employment.
- Some roles may require occasional work from an assigned office depending on job responsibilities.
Additional Information
- The employer is committed to providing an inclusive interview experience and reasonable accommodations for candidates with disabilities.
- For U.S. positions potentially based in Los Angeles or San Francisco, qualified applicants with arrest and conviction records will be considered in accordance with local fair chance hiring ordinances.