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Salary: USD 100,000 - 120,000 / annual
Upgrade is seeking a Data Scientist to join the Flex Pay team (buy now pay later sector). You will build and maintain predictive risk models, optimize offers and pricing, and extract insights that drive product development, risk mitigation, and customer strategy.
Key Responsibilities:
- Build and maintain credit and fraud policy simulators to ensure properly functioning systems and identify risk decisioning enhancements
- Build and deploy statistical models and machine learning algorithms to solve business problems in credit risk, fraud detection, pricing, customer segmentation, and marketing attribution
- Validate models to identify factors affecting model performance
- Analyze large, structured and unstructured datasets using SQL, Python, or similar tools
- Stay current with latest trends and technologies in data science and fintech; actively research new tools and techniques for model development
- Collaborate with cross-functional teams (risk, marketing, product, engineering) to define data-driven strategies
The role is based in the San Francisco office on a hybrid schedule, specifically Wednesdays and Thursdays.
Requirements:
- Advanced degree (MS/PhD) in Data Science, Statistics, Mathematics, Computer Science, Finance, or related quantitative discipline
- 2+ years of hands-on experience in a data science or analytics role, preferably in financial services
- Strong proficiency in Python (Pandas, Numpy, Scikit-learn) and SQL
- Experience and/or strong interest in machine learning techniques (Random Forest, Gradient Boosted Trees, etc.)
- Ability to write documentation and present analysis to people with different levels of expertise
- Proactive, driven, and able to work in a fast-paced environment
Nice to Have:
- Experience with data visualization tools (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
