Backed by USV.
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. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 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
More about this 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.
This position is based in our San Francisco office in a hybrid capacity, specifically on Wednesdays and Thursdays.
- 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.
-...
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