Data Scientist - Flex Pay

Upgrade•San Francisco, CA

8d•$100,000 - $120,000•Hybrid

About The Position

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. This position is based in our San Francisco office in a hybrid capacity, specifically on Wednesdays and Thursdays.

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.
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.

Nice To Haves

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

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.