Senior Data Scientist

We are seeking a highly analytical and results-driven Data Scientist to join our buy now pay later (BNPL) sector called Flex Pay. You will play a key role in building predictive risk models, optimizing offers and pricing, and extracting insights that drive product development, risk mitigation, and customer strategy. This role requires a strong foundation in statistics, machine learning, and programming.

While this position is ideally based in our San Francisco office on a hybrid basis, we are also open to considering remote candidates.

- Build and maintain credit and fraud policy simulators used to ensure properly functioning systems and identify risk decisioning enhancements.

- Build and deploy statistical models and machine learning algorithms to solve business problems in areas like credit risk, fraud detection, pricing, customer segmentation, and marketing attribution.

- Validate models to identify factors that may affect model performance.

- Analyze large, structured and unstructured datasets using SQL, Python or similar tools.

- Stay up to date with the latest trends and technologies in data science and fintech, actively research new tools and techniques available for model development.

- Collaborate with cross-functional teams including risk, marketing, product, and engineering to define data-driven strategies.

What We Look For

- Advanced Degree (MS/PhD) in Data Science, Statistics, Mathematics, Computer Science, Finance, or a related quantitative discipline.

- 3-5+ years of hands-on experience in a data science or analytics role, preferably in financial services.

- Experience and/or strong interest in machine learning techniques (Random Forest, Gradient Boosted Trees, etc.) strongly preferred.

- Strong proficiency in Python (Pandas, Numpy, Scikit-learn) and SQL.

- Ability to write documentation and present analysis to people with different levels of expertise (e.g., technical staff, business leads, etc.).

- Proactive, driven, and ability to work in a fast paced environment.