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About the Role
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.
Responsibilities
- 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.
Requirements
- Advanced Degree (MS/PhD) in Data Science, Statistics, Mathematics, Computer Science, Finance, or a related quantitative discipline.
- 2 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.
- Experience with data visualization tools (e.g., Tableau, Power BI).
- Experience with AI tools such as Claude.
- Experience with data technologies like PySpark.
- Understanding of financial services concepts such as credit scoring, portfolio risk, or customer lifetime value.
Skills
- Statistics
- Machine Learning
- Programming
- Python
- SQL
- Pandas
- Numpy
- Scikit-learn
- Data Visualization
- Tableau
- Power BI
- AI Tools
- Claude
- PySpark
- Credit Scoring
- Portfolio Risk
- Customer Lifetime Value
Location
- San Francisco, CA
Work Type
- Hybrid
Experience Level
- 2 years
Education Level
- MS/PhD
Salary/Compensations
- USD $100,000-$120,000 annually
Benefits
- Competitive salary and stock option plan
- Paid coverage of medical, dental and vision insurance
- Competitive 401(k) and RRSP program
- Flexible PTO
- Opportunities for professional growth and development
- Paid parental leave
- Health & wellness initiatives
- Equity
About the Company
- Our buy now pay later (BNPL) sector is called Flex Pay.